MCP Tool Design

MCP Tool Design

A well-designed tool is invoked correctly by the agent on the first try. A bad one causes loops, wrong-tool selection, or hallucinated arguments.

When to Use

• Adding a new tool to an MCP server
• Debugging "the model never picks this tool" or "the model picks the wrong tool"
• Reviewing a PR that adds tools
• Cleaning up a server with 30+ tools

The Three Levers

Agents pick tools based on name, description, and parameter schema — in that order of signal strength. Every design choice should strengthen at least one.

Naming Rules

Rule: <verb>_<object>[_<qualifier>]. If two tools could answer the same query, one has the wrong name.

Description Rules

Descriptions are what the model reads most carefully. Budget ~1-3 sentences:

<Verb-led action>. <When to use it / when NOT to use it>. <Any gotchas>.

Example — weak vs strong

Weak:

Creates an issue.

Strong:

Create a GitHub issue in the specified repo. Use this for new bug reports or feature requests. Do NOT use to comment on an existing issue — use create_issue_comment for that. Title is required; body supports markdown.

The "when NOT to use" line is the highest-leverage sentence you can write — it routes disambiguation without the agent needing to enumerate all tools.

Parameter Schema Rules

1. Describe every field — .describe() in Zod, Field(description=...) in Pydantic. Undescribed fields get hallucinated values
2. Use enums for closed sets — priority: z.enum(["low", "med", "high"]) beats priority: z.string()
3. Default the optional — if 80% of calls use limit=50, set the default; don't make the agent guess
4. Prefer primitives at the top level — nested objects increase hallucination rates
5. Avoid freeform "options" bags — split into discrete flags

Example — good schema

server.tool(
  "search_issues",
  "Search GitHub issues across a repo. Returns up to 50 issues matching the query. " +
  "Use for finding issues by keyword or label. For a specific issue by number, use `get_issue` instead.",
  {
    repo: z.string().describe("owner/name format, e.g. 'anthropic/claude-code'"),
    query: z.string().describe("Full-text search query, GitHub search syntax supported"),
    state: z.enum(["open", "closed", "all"]).default("open"),
    labels: z.array(z.string()).optional().describe("Filter by label names (AND semantics)"),
    limit: z.number().int().min(1).max(100).default(25),
  },
  async (args) => { /* ... */ },
);

Return Shape Rules

The model sees the tool result as text. Structure matters:

1. Lead with a summary line — "Found 3 open issues matching 'auth bug':"
2. Include IDs so the model can chain calls
3. Truncate aggressively — don't return 200 rows when 10 will do; include "... 190 more, use offset=10" hint
4. Never return raw HTML — strip or convert to markdown

return {
  content: [{
    type: "text",
    text: [
      `Found ${results.length} issues matching "${query}":`,
      ...results.slice(0, 10).map(r => `- #${r.number} ${r.title} (${r.state})`),
      results.length > 10 ? `... ${results.length - 10} more. Narrow query or paginate with offset.` : "",
    ].filter(Boolean).join("\n"),
  }],
};

Surface Size

Tool selection accuracy degrades past ~15 tools per connected server set. If you have 40 CRUD operations, collapse them:

• Before: create_issue, update_issue, delete_issue, close_issue, reopen_issue, assign_issue, ... (12 tools)
• After: issue_action(action: "create"|"update"|"close"|"reopen", ...) (1 tool with discriminated schema)

Only collapse if the sub-actions share 80%+ of their schema. Otherwise the union becomes a mess.

Anti-Patterns

1. The God Tool — execute(query: string) that dispatches everything. Model can't pick wisely; hallucinates queries
2. Duplicate tools — search, find, lookup, query all doing similar things
3. Hidden required args — options: object where some keys are required; agent skips them
4. Silent truncation — returning 10 of 500 results without saying so; agent assumes it got everything
5. Tool-name collisions across servers — if user has 3 servers each with a search tool, the model gets confused. Prefix: linear_search, github_search

Validation Checklist

Before shipping a tool:

• Name is verb-led and unambiguous
• Description has ≥1 "when NOT to use" sentence if there's a nearby tool
• Every parameter has .describe()
• Closed-set fields use enums
• Defaults cover the common case
• Return includes IDs for chaining
• Return summarizes + truncates large lists
• Tested with MCP Inspector that the model can invoke it zero-shot

Best Practices

1. Read your descriptions aloud — if you stumble, the model will too
2. Pilot with one real conversation before adding 10 tools at once
3. When a user reports "the agent won't use my tool", check description first, schema second, name third
4. Log unresolved tool calls (agent tried to call a non-existent tool) — it reveals the tool it WANTED