Design MCP Server

When to Use

• User says "I want to build a ___ MCP server"
• User has an API, database, or system they want to expose to LLMs
• User wants to plan tools before scaffolding
• Existing server needs a new capability area (design the addition, not just a single tool)

Do NOT use for single-tool additions — use add-tool directly.

Inputs

Gather before designing. Ask the user if not obvious from context:

1. Domain — what system, API, or capability is this server wrapping? Or is the server providing internal capability with no external dependency (computation, text/code utilities, in-memory state)?
2. Data sources / source of truth — APIs, databases, file systems, external services? Or is the server itself the source (in-memory state, pure computation, local-only utility, embedded model)?
3. Target users — what will the LLM (and its human) be trying to accomplish?
4. Scope constraints — read-only? write access? admin operations? what's off-limits?

If the domain has a public API, read its docs before designing. For internal-only servers, skip API research and go straight to user goals. Don't design from vibes either way.

Server scope and audience

Before committing to a server boundary, answer: what workflow does this server serve, and who is the audience?

The unit of a server is a user workflow, not an API. A single rich API can earn its own server when the audience is large and the API surface supports a full workflow (PubMed for literature research, SEC EDGAR for financial analysis, Shodan for internet-wide device intelligence). Multiple APIs should collapse into one server when they serve the same workflow from different angles — a "threat intelligence" server that aggregates VirusTotal, AbuseIPDB, and GreyNoise is more useful than three separate servers because the user's goal is "assess this indicator," not "query VirusTotal."

Don't default to one-API-one-server. That's the right call when the API is deep enough and the audience is large enough, but it's not the starting point. The starting point is the workflow:

When multiple APIs collapse into one server, the tool surface is organized around what the user is doing, not which API gets called. The agent says "investigate this domain" and the server routes to the best available source internally. Individual APIs become service-layer implementation details, not tool-surface identities.

Server Naming

The server name (repo name, npm package, public identity) must communicate what it does at a glance. The test: can a human or agent scanning a server list tell what this server does from the name alone?

• Use the canonical platform/brand name, not abbreviations. libofcongress-mcp-server not loc-mcp-server ("loc" reads as lines-of-code or location). federal-reserve-mcp-server not fred-mcp-server ("fred" reads as a person's name).
• Add a descriptive suffix when the base name is a non-obvious acronym. Pattern: {acronym}-{domain}-mcp-server — e.g., eia-energy-mcp-server, bls-labor-mcp-server, nhtsa-vehicle-safety-mcp-server. Skip when the name is already self-descriptive (earthquake-mcp-server, wikidata-mcp-server).
• Don't overclaim scope. A name asserts breadth on two independent axes, and must be honest on both. Source breadth: is this a first-party wrapper around one provider's API, or genuine aggregation across several independent sources behind one normalized surface? Domain or jurisdiction breadth: one, or many? A generic name claims breadth on whichever axis it leaves unqualified. threat-intel-mcp-server earns its generic name by aggregating independent sources for one workflow; that same name over a single vendor's API would be a defect — name that for the vendor. When the real scope is one jurisdiction, put it in the name (uk-legislation-mcp-server, statistics-canada-mcp-server) rather than letting a generic noun imply worldwide coverage a user will only discover is absent after installing. Note that aggregating several bodies within one jurisdiction earns a cross-source name, not a cross-jurisdiction one.
• Aggregation is a claim about entities, not endpoints. One provider publishing five APIs is still first-party. Wrapping several endpoints of the same upstream does not make a server an aggregator and does not earn a generic name.
• The tool prefix derives from the name but is a separate identifier. Every tool is {prefix}_{verb}_{noun}, so the prefix shows up in every tool call an agent sees, and a descriptive one gives agents domain context without reading the server's instructions. The prefix names the source, dropping the descriptive qualifier the repo name carries for human browsing: eia-energy-mcp-server → eia_, nhtsa-vehicle-safety-mcp-server → nhtsa_. Because it names the source rather than the repo, the two can diverge — a later repo rename does not have to move the prefix, and usually shouldn't: renaming an advertised tool surface is a breaking change for every existing client, while renaming a package is not.

Steps

1. Research External Dependencies

Applies when: the server wraps an external API or service. Skip for internal-only servers (computation, local file ops, in-memory state, code analysis utilities) and jump to Step 2.

Before designing, verify the APIs and services the server will wrap. Read the docs, then hit the API — real requests reveal what docs omit.

Research inline by default — fetch docs, read SDK readmes, confirm assumptions before committing them to the design. For each external dependency:

• Fetch API docs, confirm endpoint availability, auth methods, rate limits
• Check for official SDKs or client libraries (npm packages)
• Note any API quirks, pagination patterns, or data format considerations

When research is genuinely parallelizable (multiple independent APIs, several SDKs to evaluate), spawn background agents for the independent legs while you proceed with domain mapping. Skip the overhead for a single API — just read it yourself.

Live API probing. After reading docs, make real requests against the API to verify assumptions:

• Response shapes — confirm actual field names, nesting, and types. Docs frequently lag or omit fields.
• Batch/filter endpoints — look for filter.ids, bulk GET, or query-by-multiple-IDs patterns. A single batch request replaces N individual fetches and eliminates serial-request bottlenecks and rate-limit accumulation.
• Field selection — check if the API supports fields or select parameters to request only the data you need. This reduces payload size dramatically for large objects.
• Pagination behavior — verify token format, page size limits, and what happens when results exceed one page.
• Error shapes — trigger real 400/404/429 responses to see the actual error format, not just what docs claim.
• Unknown-param behavior — send one deliberately misspelled parameter. If the API silently ignores it (plausible but unfiltered results instead of an error), every typo'd or unverified param name becomes a silent-wrongness bug — the service layer then needs a strict allowlist of confirmed spellings, and new filters require a probe before they ship.
• Omission semantics — for each major optional parameter, check what omitting it actually returns. Some APIs default to the intuitive scope; others silently widen (all historical versions, all statuses, global instead of regional). A default that changes result meaning becomes a server-side default plus an echoed output field, not something left to the agent.

Stopping condition: at minimum, probe one list/search endpoint, one single-item GET, one error case (force a 404 or 400), and one unknown-param request. For large APIs with many resource types, add one probe per major noun. Stop when the response shapes and error envelope are confirmed.

This step prevents building a service layer against assumed response shapes that don't match reality.

2. Map User Goals, Then Domain Operations

Start with user goals, not endpoints. Enumerate the outcomes an agent (and its human) will actually try to accomplish with this server — usually 3–10, scaled to domain size. These drive the workflow tools that form the spine of the surface. Endpoint-inventory-first design produces 1:1 API mirrors; goal-first design produces tools agents reach for. For internal-only servers, goals map to capabilities rather than endpoints — e.g., "format markdown to GFM," "tokenize text by model," "compute file hash."

Example user goals for a project management server:

• Find tasks I'm assigned to that are due soon
• Create a task in a project, assign it, and notify the owner
• Mark a task complete and log the outcome
• Audit a project's overdue work

Then enumerate the underlying domain operations the system supports, grouped by noun. These are the raw material workflow tools compose and single-action tools back-fill where workflows don't cover an edge case.

The user-goal list shapes the tool surface; the operation list fills in the gaps. Not every operation becomes a tool — an operation stays as raw material (not its own tool) when it's already fully covered by an existing tool's output, or when the only agents who'd use it are in scenarios outside this server's stated purpose.

3. Classify into MCP Primitives

Tools are the primary interface. Not all MCP clients expose resources — many are tool-only (Claude Code, Cursor, most chat UIs). Design the tool surface to be self-sufficient: an agent with only tool access should be able to do everything the server is built for. Resources add convenience for clients that support them (injectable context, stable URIs), but are not a reliable access path.

What the tool surface needs to cover depends on the server: a read-only research server has different economics than a CRUD project management server. Consider the domain, the expected agent workflows, whether it wraps one API or many, and what data relationships exist. The test is: can a tool-only agent accomplish everything this server is for?

Common traps:

• Data locked behind resources: If something an agent needs is only accessible via a resource, it's invisible to tool-only clients. That data might warrant its own tool, or it might already be covered by an existing tool's output — but it needs a tool path somewhere.
• CRUD explosion: Don't map every REST endpoint to a tool. Related operations on the same noun often belong in one tool with an operation/mode parameter (see Step 4).
• 1:1 endpoint mirroring: API endpoints are designed for programmatic consumers. LLM tools should be designed for workflows — what an agent is trying to accomplish, not what HTTP calls happen under the hood.

Irreversible operations stay in the UI. The "Neither" bucket above covers operations that aren't useful to an LLM. There's a second, sharper reason to exclude something from the tool surface: operations whose failure mode is catastrophic and unrecoverable. Examples span domains — dropping a production database table (data loss across every row), force-emptying a versioned cloud-storage bucket (no recovery once the lifecycle policy fires), revoking the workspace's last admin role (locks everyone out, recovery requires vendor support), GDPR permanent-delete on a customer profile (un-restorable by design), purging an analytics warehouse partition older than the retention window (auditable history gone), or deleting the single audience on a free-plan email platform (nukes every subscriber and historical report in one call). These are useful to an LLM in principle, but the blast radius of a mis-call is disproportionate to any agent workflow. Humans do these in the vendor UI, where confirmation dialogs and undo paths exist. Agents shouldn't have the tool at all.

This is distinct from destructiveHint — that annotation is for operations that are destructive but recoverable (deleting a task, reverting a commit) and agents should still have them. The "stays in the UI" line applies only to operations whose failure is both catastrophic and irreversible.

4. Design Tools

This is the highest-leverage step. Tool definitions — names, descriptions, parameters, output schemas — are the entire interface contract the LLM reads to decide whether and how to call a tool. Every field is context. Design accordingly.

Tool shapes you'll encounter

Most tools follow the {server}_{verb}_{noun} default — one focused responsibility, one clear verb, often (but not always) one upstream call. API-wrapping examples: pubmed_search_articles, pubmed_fetch_articles. Internal-only examples: markdown_format_text, regex_test_pattern, tokens_count_text — same naming convention, no external dep. Two variants warrant explicit design pressures of their own:

These aren't boxes every tool must fit into — some blend shapes — but the design pressures differ enough that naming them helps avoid re-discovering the patterns per server. The subsections below cover considerations specific to each — workflow framing applies broadly; instruction tools, reference tools, and workflow safety are their own subsections.

Think in workflows, not endpoints

The unit of a tool is a useful action, not an API call. Ask: "What is the agent trying to accomplish?" — not "What endpoints does the API have?"

A single tool can call multiple APIs internally, apply local filtering, reshape data, and return enriched results. The LLM doesn't know or care about the underlying calls.

// Workflow tool — search + local filter pipeline, not a raw API proxy
const findStudies = tool('clinicaltrials_find_studies', {
  description: 'Matches patient demographics and medical profile to eligible clinical trials. Filters by age, sex, conditions, location, and healthy volunteer status. Returns ranked list of matching studies with eligibility explanations.',
  // handler: listStudies() → filter by eligibility → rank by location proximity → slice
});

Tip — mode consolidation. When a tool has several related operations on the same noun, you can consolidate them under one tool with a mode/operation enum. This affects both naming (noun-led, e.g., github_pull_request) and handler design (dispatch by mode). Use when it tightens the surface; skip when ops diverge enough to warrant separate tools.

Multi-source tools and fallback chains

Applies when: a server aggregates multiple data sources for the same workflow, and the "best" source varies by input type, availability, or coverage. Skip for single-API servers.

When a tool's goal can be served by multiple sources, design it as a multi-source tool — the agent calls one tool, the handler routes to the best source (or fans out to several) internally. This is the difference between a "PubMed wrapper" and a "literature research server": pubmed_search_articles tries PubMed first, falls back to EuropePMC for broader coverage, then Unpaywall for open access. The agent doesn't choose which API to hit — the server makes that decision based on what works.

Two patterns:

Source fallback chains — try sources in priority order, fall through on failure or empty results. Best when sources cover the same data with different depth or availability. The output should indicate which source provided the data so the agent (and human) can assess provenance.

// Handler pseudocode — not a real implementation
async handler(input, ctx) {
  // Primary: PubMed E-utilities (authoritative, best metadata)
  const result = await pubmedService.search(input.query);
  if (result.items.length > 0) return { ...result, source: 'pubmed' };

  // Fallback: EuropePMC (broader coverage, includes preprints)
  const epmcResult = await epmcService.search(input.query);
  if (epmcResult.items.length > 0) return { ...epmcResult, source: 'europepmc' };

  return { items: [], source: 'none', message: 'No results from any source.' };
}

Multi-source fan-out — query multiple sources in parallel, merge results. Best when sources provide complementary data about the same entity. Use Promise.allSettled so one failing source doesn't tank the whole call.

// Handler pseudocode — indicator enrichment across threat intel sources
async handler(input, ctx) {
  const [vt, abuse, greynoise] = await Promise.allSettled([
    vtService.lookup(input.indicator),
    abuseIpService.check(input.indicator),
    greynoiseService.query(input.indicator),
  ]);
  return {
    indicator: input.indicator,
    sources: {
      virustotal: vt.status === 'fulfilled' ? vt.value : { error: vt.reason.message },
      abuseipdb: abuse.status === 'fulfilled' ? abuse.value : { error: abuse.reason.message },
      greynoise: greynoise.status === 'fulfilled' ? greynoise.value : { error: greynoise.reason.message },
    },
    // Server synthesizes a verdict from available data — the agent gets a conclusion, not raw API dumps
    assessment: synthesizeVerdict(vt, abuse, greynoise),
  };
}

In both patterns, the tool surface is organized around what the user is doing. Sources are service-layer details — the agent sees threat_enrich_indicator, not virustotal_lookup + abuseipdb_check + greynoise_query. Mode-based dispatch by input type (e.g., indicator_type: 'ip' | 'domain' | 'hash') naturally routes to different source chains per mode, since different sources cover different indicator types.

There is no fixed ceiling on tool count — tools need to earn their keep, but don't artificially limit the surface. If the domain genuinely has 20 distinct workflows, expose 20 tools.

Cut the surface

After mapping tools, review the full list critically. A tool that covers a niche use case, serves a tiny fraction of agents, or duplicates what another tool already handles is a candidate for deferral. Drop it from the design and note it as a future addition if demand warrants. Every tool in the surface is cognitive load for tool selection — a tight surface outperforms a comprehensive one.

Instruction tools

Applies when: the domain has recurring "how do I do X well given my current state" questions worth merging with static procedural content. Skip otherwise.

Some domains benefit from a tool whose output is guidance, not data — a markdown playbook tailored by live account state, with pre-filled next-step tool calls. These sit between Prompts (static templates, client-invokable) and action tools (do work, return data): they return advice, but the advice is worth more than static text because it merges procedural content with the agent's actual situation.

Characteristics:

• Output is markdown guidance, not structured data (though the output schema still has fields — typically guidance, diagnostics, and nextToolSuggestions)
• Merges static procedural content with live state — the value is the tailoring. "You have 12 staged files spanning 4 unrelated changes — split them into separate commits before pushing" beats a generic best-practices article. The same shape works in other domains: "Your slowest query is 2.3s on orders.customer_id — add the index before tuning the planner" (database advisor), "Error rate spiked 4× at 14:32 UTC, 4 minutes after the web@a3f9c2 deploy — roll back before chasing the upstream provider" (incident triage).
• readOnlyHint: true, openWorldHint: false — no writes, deterministic given the same inputs and account state
• Outputs nextToolSuggestions — an array of recommended follow-up tool calls with arguments pre-filled from the diagnostics, not just tool names. The agent consumes the playbook, then executes steps with other tools.
• Consolidate by topic enum — what could be N separate per-topic tools collapses into one

const wrapupInstructions = tool('git_wrapup_instructions', {
  description: 'Procedural guidance tailored to current repo state. Returns best-practice markdown merged with live diagnostics (staged/unstaged files, branch info, recent commits) and pre-filled follow-up tool calls. Read-only; the agent then executes steps with other tools.',
  annotations: { readOnlyHint: true, openWorldHint: false },
  input: z.object({
    topic: z.enum(['review-changes', 'stage-and-commit', 'push-to-remote'])
      .describe('Playbook topic. Determines which static guidance is returned and which live state is fetched for tailoring.'),
  }),
  output: z.object({
    guidance: z.string()
      .describe('Markdown playbook content, tailored to current account state.'),
    diagnostics: z.record(z.unknown())
      .describe('Live state used to tailor the guidance (e.g., staged file count, branch divergence, recent commit cadence).'),
    nextToolSuggestions: z.array(z.object({
      toolName: z.string().describe('Tool to call next.'),
      reason: z.string().describe('Why this step is recommended given current state.'),
      args: z.record(z.unknown()).describe('Arguments pre-filled from diagnostics.'),
    })).describe('Recommended follow-up calls with arguments already populated.'),
  }),
});

Prior art: git_wrapup_instructions walks through staging, commit, and push with repo state inspected. If a server has recurring "how do I do X well given my state" questions, an instruction tool typically beats N topic-specific tools and duplicating guidance in tool descriptions.

Suggestions are scoped to what this deployment registers. A nextToolSuggestions entry is an executable call, so it is only correct when its target is enabled under the same configuration — a tool wrapped in disabledTool() is absent from tools/list, and a suggestion naming it hands the agent a call that fails on dispatch. Build the array from the same config the registration reads, and when the target is off, drop the entry rather than the explanation: guidance can still say the capability is unavailable in this deployment and what to do instead. The audit and a worked example live under Feature-flagged tools in add-tool/SKILL.md.

Reference tools

Applies when: the domain speaks in opaque vocabulary — enum codes, classification systems, identifier formats, per-source coverage windows — that agents must supply as inputs elsewhere. Skip when inputs are self-evident (free text, ISO dates, well-known formats).

A reference tool is the surface's decoder ring: which codes exist, what they mean, what format each identifier takes, what each source covers. Consolidate what could be N per-list tools under one topic enum, and serve it from static tables or a cached fetch so it stays cheap to call. Its second job is structural: it is the standing target of recovery routing — error recovery strings, zero-hit notices, and resolver guidance across the surface end with "consult the reference tool, topic X" (see Error design), which only works when the tool exists. Implement it first — it usually has no service dependency, and it grounds field-testing for every other tool.

Workflow tool safety

Applies when: a tool performs multi-step mutations with destructive modes (send/apply/promote) that benefit from human confirmation before the irreversible step fires. Skip for read-only or idempotent workflows.

Tools that perform multi-step mutations (the Workflow shape) have two safety considerations beyond single-call tools. Both are about giving the agent — and the human behind it — a chance to catch a bad invocation before it commits.

Confirmation-gated destructive modes, with an annotation fallback. When a workflow's mode parameter switches between safe and destructive arms (draft vs send, plan vs apply), gate the destructive arm on a confirmation the handler asks for via ctx.requestInput(...), so a human approves before the irreversible step fires. The handler is re-entered with the answer on ctx.inputs; it does not await mid-call.

The gate is always reachable — ctx.requestInput is present on every transport and both protocol eras — but it is not always answerable: a client that never fulfils the input_required result simply doesn't retry, and the destructive step never runs. The same holds for a 2025-era HTTP client when the server runs MCP_SESSION_MODE=stateless, which disables the legacy round-trip shim — the gate refuses and the destructive step never fires. That is the safe outcome, but it makes the tool unusable for those clients, so weigh it before defaulting such a server to stateless (api-context § ctx.requestInput). Keep destructiveHint: true in annotations so those clients' own approval flows still surface the risk.

annotations: { destructiveHint: true },        // client-side approval flows still see the risk
// ...
const Confirm = z.object({ confirmed: z.literal(true).describe('Type true to apply.') });

handler(input, ctx) {
  if (input.mode === 'apply') {
    // A decline is terminal — re-asking would loop until the round budget runs out.
    const view = ctx.inputs.view('confirm');
    if (view.kind === 'elicit' && view.action !== 'accept') {
      throw validationError('Migration cancelled by user.');
    }
    if (!ctx.inputs.accepted('confirm', Confirm)) {
      return ctx.requestInput({
        inputRequests: {
          confirm: inputRequired.elicit({
            message: `Apply migration affecting ${affectedRowCount} rows in production? Cannot be rolled back automatically.`,
            requestedSchema: Confirm,
          }),
        },
      });
    }
  }
  // destructive step proceeds
}

Safe defaults on parameters that determine blast radius. When a workflow accepts a parameter that controls how far-reaching a mutation is, default to the safer value. A bulk file-update tool defaulting mode: 'preview' (no writes) means a sloppy agent call shows a diff rather than blasting changes; an apply-plan tool defaulting dryRun: true means a misread plan previews rather than executes; an object-delete tool requiring an explicit confirmCount matching the result-set size means an unscoped query can't silently nuke a million rows. Agents that genuinely want the destructive behavior have to name it explicitly, which surfaces intent in the tool call and in logs.

Tool descriptions

The description is the LLM's primary signal for tool selection. It must answer: what does this do, and when should I use it?

• Be concrete about capability. "Search for clinical trial studies using queries and filters" beats "Interact with studies."
• Include operational guidance when it matters. If the tool has prerequisites, constraints, or gotchas the LLM needs to know, say so in the description. Don't add boilerplate workflow hints when the tool is self-explanatory.
• Prefer a single cohesive paragraph. Pack operational guidance into prose sentences (separated by periods or em-dashes) rather than bullet lists or blank-line-separated sections. Descriptions render inline in most clients, and bullet structure reads as visual noise rather than signal. Operation-by-operation bullets also duplicate info that already lives in the operation enum's .describe().
• Don't leak. Descriptions are for the consumer, not the author. Three categories to audit against:
  • Implementation details — endpoint paths, API call counts, internal parameter mappings, routing logic. Describe what the tool does and when to use it, not how it's wired up.
  • Meta-coaching — directives about how to use the output. "Treat X as the canonical Y", "callers should…", "the LLM should…". The description sells the tool; it doesn't coach the reader.
  • Consumer-aware phrasing — references to "LLM", "agent", "Claude", or any specific reader. The description shouldn't name who's reading it.

// Good — describes a prerequisite the LLM must know
description: 'Set the session working directory for all git operations. This allows subsequent git commands to omit the path parameter.'

// Good — self-explanatory, no workflow hints needed
description: 'Show the working tree status including staged, unstaged, and untracked files.'

// Good — warns about constraints
description: 'Fetches trial results data for completed studies. Only available for studies where hasResults is true.'

Descriptions should be as long as needed — concise but complete. Don't artificially truncate, and don't pad with filler.

Parameter descriptions

Every .describe() is prompt text the LLM reads. Parameters should convey: what the value is, what it affects, and (where non-obvious) how to use it well.

• Constrain the type. Enums and literals over free strings. Regex validation for formatted IDs. Ranges for numeric bounds.
• Use JSON-Schema-serializable types only. The MCP SDK serializes schemas to JSON Schema for tools/list. Types like z.custom(), z.date(), z.transform(), z.bigint(), z.symbol(), z.void(), z.map(), z.set() throw at runtime. Use structural equivalents (e.g., z.string().describe('ISO 8601 date') instead of z.date()).
• Explain costs and tradeoffs when a parameter choice has meaningful consequences.
• Name alternative approaches when a simpler path exists.
• Include format patterns for structured values, but don't pad descriptions with redundant examples.

// Good — explains cost, recommends action, names the alternative
fields: z.array(z.string()).optional()
  .describe('Specific fields to return (reduces payload size). Without this, the full study record (~70KB each) is returned. Use full data only when you need detailed eligibility criteria, locations, or results.'),

// Good — explains what the flag does AND how to override
autoExclude: z.boolean().default(true)
  .describe('Automatically exclude lock files and generated files from diff output to reduce context bloat. Set to false if you need to inspect these files.'),

// Good — names the format and gives one example
nctIds: z.union([z.string(), z.array(z.string()).max(5)])
  .describe('A single NCT ID (e.g., "NCT12345678") or an array of up to 5 NCT IDs to fetch.'),

Output design

The output schema and format function control what the LLM reads back. Design for the agent's next decision, not for a UI or an API consumer. See the add-tool skill's Tool Response Design section for implementation-level patterns (partial success, empty results, metadata, context budget).

Principles:

• Server reports what only the server can know; agent decides what only the agent can know. Schema, scopes, rate limits, and raw observable state belong to the server. Semantic correctness, intent-vs-effect matching, and recovery choice belong to the agent. For mutators, this means surfacing pre/post observable state rather than throwing on synthetic deltas the server can't authoritatively classify — file shrunk could be deliberate truncation or a bug; only the agent knows. See add-tool skill's Mutator response design.
• Include IDs and references for chaining. If the agent might act on a result, return the identifiers it needs for follow-up tool calls.
• Curate vs. pass-through depends on domain. Medical/scientific data — don't trim fields that could alter correctness. CRUD responses — return what the agent needs, not the full API payload. Match fidelity to consequence.
• Surface what was done, not just results. After a write operation, include the post-state so the LLM can chain without an extra round trip.
• Seed orientation context alongside the primary result. When a tool's call position makes the agent's next moves predictable, attaching a compact snapshot of relevant state — recent activity, tracked state, a couple of reference items — both saves round-trips and primes the LLM on the project's patterns. Surfacing recent commits teaches the commit-message style the agent should match when it later writes one; recent tags teach the versioning convention; reference records teach the naming format. Common fits: tools that open or close a session (set working dir, wrap-up), state-changing verbs where the caller wants post-action confirmation (commit, push, merge), entry points that drop the agent into a new scope (clone, checkout). Gather sub-operations in parallel with Promise.allSettled so a single failure degrades to a warning rather than tanking the outer call.
• Communicate filtering. If the tool silently excluded content, tell the LLM what was excluded and how to get it back. The agent can't act on what it doesn't know about.

// git_diff — when lock files are filtered, the output tells the LLM
output: z.object({
  diff: z.string().describe('Unified diff output.'),
  excludedFiles: z.array(z.string()).optional()
    .describe('Files automatically excluded from the diff (e.g., lock files). Call again with autoExclude=false to include them.'),
}),

• Empty results are a designed surface, not a fallthrough. For every search/list tool, spec the zero-hit behavior at design time: zero hits are success + an enrichment notice, never an error, and the notice is composed from condition → fragment pairs (too-narrow filter, a defaulted date window, a syntax trap), each routing to a concrete next call — relax a named filter, switch to the named sibling tool, or consult the reference tool. Relatedly, when the server applies a default that changes result semantics (an as-of date, an implicit status filter), echo the applied value in the output — an agent can't reason about a filter it can't see.
• Capped lists disclose truncation. When a tool accepts a cap-like input (limit, per_page, page_size, max_results, max_items) and returns an array, the handler must disclose when the cap was hit. Standard fields: truncated: true, shown, cap in the enrichment block via ctx.enrich.truncated({ shown, cap }). ctx.enrich.total(n) (writes totalCount) is also recognized. Silent caps leave the agent treating a partial set as complete. The capped-list-no-truncation lint rule enforces this; see api-linter and api-context's ctx.enrich.truncated() section.
• Truncate large output with counts. When a list exceeds a reasonable display size, show the top N and append "...and X more". Don't silently drop results.
• Spill big analytical results to a queryable surface. When a tool's row set is something an agent would run SQL over (aggregate, group, join) and can exceed any reasonable context budget — paginated APIs, streamed exports, big query results — pair an inline preview with a DataCanvas table holding the full set. Two rules gate this: (1) it must earn its keep on shape, not size — a discovery/search surface of categorical metadata (titles, IDs) is not analytical and doesn't get a canvas regardless of row count; for name→ID resolution over a bounded list use MCP-side list filtering; (2) the canvas_id is reachable only if the same server also exposes a dataframe_query tool — emit one without the other and the handle is dead output. Compute distributions or refinement hints across the full result, not the preview, so aggregate signal stays honest. See api-canvas for the spillover() helper and both rules in full.
• Outline one large document into sections. When a single tool call returns one document-shaped record (not many rows) that can exceed context — a ~130KB FDA drug label, a big API entity dominated by a few fat fields — return a section outline (top-level keys + per-section byte size) instead of truncating, and let the agent re-call with sections: [...] to pull only what it needs. The outlineOnOverflow() helper (@cyanheads/mcp-ts-core/utils) measures the payload and returns a full | outline result; declare the tool's output as a flat z.object with a kind discriminator and presence-based optional arms (folding in OUTLINE_VARIANT.shape.sections / .notice) — tool() rejects a z.discriminatedUnion output — so format()-parity holds when each arm renders on field presence. Pure measure + key-slice — Workers-portable, unlike canvas-bound spillover(). Distinct from spillover on shape: spillover splits a row collection, this outlines one fat record. See the techniques skill's outline-on-overflow reference.
• Mirror a bulk upstream instead of paginating it live. When the server wraps a large or slow API whose corpus is queried far more than it changes, sync it once into a persistent local index and query that as the primary data path — not the live API per request. Match the backend to corpus size: ≲ tens of thousands of rows → an in-memory index (server-level, no primitive); ~10⁴–10⁷ → the MirrorService (embedded SQLite + FTS5; declare a schema + a sync ingester via defineMirror

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