MCP Knowledge Graph

mcp-knowledge-graph — Persistent Codebase Knowledge

When to invoke

Use this skill when:

• Starting work on a codebase you want to remember across sessions
• Extracting and persisting architectural facts (components, dependencies, decisions)
• Building a project knowledge graph from a repomix-packed codebase
• Querying previously stored architectural context instead of re-reading files

Do not use for: general conversation memory (use episodic-memory), temporary session notes, or storing raw code (store facts about code, not the code itself).

The combo workflow: repomix → analyze → knowledge-graph

1. repomix.pack_codebase()           ← get full codebase content
2. grep/read the output              ← extract architectural facts
3. aim_memory_store()                ← persist entities (components, modules, services)
4. aim_memory_link()                 ← persist relations (depends_on, calls, owns)
5. aim_memory_add_facts()            ← add observations to existing entities

In subsequent sessions: skip steps 1-2, query directly:

6. aim_memory_search("auth")         ← find relevant entities by keyword
7. aim_memory_get(["AuthService"])   ← exact lookup of known entity

This saves repacking the entire codebase every session — the graph acts as a persistent index.

All 10 tools via mcporter

Choosing where to store: project vs global

Default: omit context:, use location: "project" — produces .aim/memory.jsonl in the project root. Commit this file to share architectural knowledge with the team. The global context-loader hook also reads this file automatically on session start.

Only use context: when you need multiple separate graphs in the same project (rare). Named contexts create memory-{name}.jsonl files which are harder to discover and don't share well.

Example: build a project knowledge graph

# Initialize once
mkdir .aim    ← creates project-local storage

# Store entities — no context: needed
mcporter call knowledge-graph.aim_memory_store(
  location: "project",
  entities: [
    {"name": "AuthService", "entityType": "service", "observations": ["Handles JWT auth", "Stateless"]},
    {"name": "UserRepository", "entityType": "repository", "observations": ["PostgreSQL", "Owns user table"]}
  ]
)

# Link them
mcporter call knowledge-graph.aim_memory_link(
  location: "project",
  relations: [
    {"from": "AuthService", "to": "UserRepository", "relationType": "depends_on"}
  ]
)

# Query later
mcporter call knowledge-graph.aim_memory_search(query: "auth")
mcporter call knowledge-graph.aim_memory_get(names: ["AuthService"])

Personal notes (not committed)

mcporter call knowledge-graph.aim_memory_store(
  location: "global",
  entities: [{"name": "my-note", "entityType": "note", "observations": ["Personal observation"]}]
)

Storage mechanics (important for correct usage)

• Project-local file: .aim/memory.jsonl — created when location: "project" and no context: given
• Global file: ~/.aim/memory.jsonl — created when location: "global"
• Auto-detection: if .aim/ exists in project root, project-local is used automatically when location is omitted
• Full rewrite: every write operation rewrites the entire file — keep graphs focused, not massive
• No concurrency: do not run two sessions writing to the same graph simultaneously
• Safety marker: every file starts with {"type":"_aim","source":"mcp-knowledge-graph"} — do not edit files manually unless you preserve this

Entity and relation design

Entity: {name, entityType, observations[]}
- name: unique identifier (e.g. "AuthService", "UserController")
- entityType: semantic category (service, repository, module, concept, decision, person)
- observations: string[] of facts — append via aim_memory_add_facts

Relation: {from, to, relationType}
- relationType: active-voice verb (depends_on, calls, owns, extends, implements, manages)
- directional: from→to and to→from are different

Naming conventions:

• Entity names: PascalCase for code components, snake_case for concepts
• Relation types: snake_case verbs

Search behavior (know the limits)

• aim_memory_search — substring match, case-insensitive, across name + type + observations
• aim_memory_get — exact name match only, case-sensitive
• No semantic/vector search — if you stored "AuthService handles JWT", searching "authentication" will NOT find it; "JWT" will
• Implication: use consistent, predictable observation wording when storing facts

Initializing a project graph

mkdir .aim

Commit .aim/memory.jsonl to share the graph with the team — it's just a JSONL file, safe to version-control.

If you want personal notes excluded from commits, add ~/.aim/ to nothing (it's already outside the repo). Only add .aim/*.jsonl to .gitignore if the graph contains secrets or machine-specific paths that shouldn't be shared.

Limitations

• Large graphs degrade performance (full in-memory load per operation)
• No append mode — each write rewrites the whole file
• No fuzzy or semantic search — keyword precision matters
• No versioning or rollback built-in
• Project detection searches up 5 directory levels for .git, package.json, .aim, etc.