Local RAG MCP

Local RAG MCP

When to use

Use when the task requires semantic search over a local document corpus and an appropriate local RAG MCP server is available. Prefer standard grep/glob for simple pattern matching — RAG adds value for conceptual queries and cross-document synthesis.

Requirements / Checks

• Verify a local RAG MCP server is configured (apx mcp list or check MCP settings).
• Do NOT attempt to install or spin up Docker containers for vector databases without explicit user permission.
• Confirm whether the embedding provider is local or remote — if remote (e.g., OpenAI), warn the user before ingesting sensitive content.

Workflow

1. Identify need — determine whether the query requires semantic retrieval (conceptual, cross-document) vs. standard grep/glob (exact pattern, single file).

2. Check configuration — verify the connection to the local RAG MCP server. If it fails, surface the error rather than falling back silently.

3. Inventory corpus — use status or list tools to see what's already indexed before ingesting anything.

4. Ingest (only if necessary) — ingest only files explicitly approved for this corpus. Include clear source metadata (file path, ingest timestamp). Exclude: .env files, credential files, SSH keys, and files outside the workspace.

5. Query strategy:

   • Start with the user's exact terms; do not paraphrase into broader concepts.
   • Add one specific disambiguating detail if initial results are too broad.
   • Keep result limits small first (top 5); expand only if results are insufficient.

6. Expand around hits — if a top result lacks surrounding context, fetch neighboring chunks before drawing conclusions.

7. Synthesize with citations — in your response, distinguish between retrieved evidence (cite source and chunk) and your own inference.

8. Clean up — delete stale or incorrectly ingested sources when requested; do not accumulate unrelated documents.

Tool Interface (illustrative — actual names depend on your server)

The local RAG server typically exposes tools along these lines:

• query: keyword + semantic search with a score/rank and result limit.
• ingest_file: absolute-path document ingestion.
• ingest_data: string/HTML/Markdown ingestion with source and format metadata.
• delete_source: remove an ingested file or source by ID.
• list_sources / status: corpus inventory and database health.
• get_neighbors: expand context around a specific chunk.

Exact tool names and schemas vary by implementation. Read your server's tool list before assuming names.

Safety Constraints

• Do NOT ingest sensitive personal data, secrets, or .env files into the local RAG store.
• Warn the user before ingesting content if the embedding model sends data to a remote API.
• Do not ingest whole repositories by default — start with approved docs or scoped folders.
• Do not treat a semantic match as ground truth without reading the source chunk in context.

Validation / Done Criteria

• Relevant context was retrieved and cited.
• Ingestion explicitly excluded sensitive paths.
• Query result synthesis distinguishes retrieved evidence from inference.

References

• references/rag-tool-model.md