mcp-snippets-server
Provides an MCP server that performs semantic search over local Markdown code-snippet files. On first run it parses `.md` snippets, generates embeddings using an OpenAI-compatible embeddings endpoint, persists a local JSON vector store, and exposes a single MCP tool `search_snippet(topic)` for topic-based retrieval.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
TLS/auth are not specified in the README. The embedding backend is configurable via `MODEL_RUNNER_BASE_URL` which defaults to localhost but could be set to an external endpoint; embeddings may expose snippet content to that service. Vector store is persisted to a local JSON file, so embeddings and snippet-derived text may remain on disk. No mention of access control, logging redaction, or rate limiting.
⚡ Reliability
Best When
You can run the service yourself (or in Docker) with controlled access to the MCP endpoint and a local/snippet-specific corpus, and you want quick semantic retrieval with persisted embeddings.
Avoid When
You need robust authentication/authorization, multi-user isolation, or strict guarantees about security/privacy behavior that aren’t documented here.
Use Cases
- • Semantic retrieval of relevant code snippets from a local knowledge base of Markdown files
- • Building an agent workflow that can ask questions like “how do I implement X in Go?” and receive the most relevant snippet(s)
- • Offline/controlled RAG search over a repository of snippet docs using an OpenAI-compatible embedding server (e.g., local llama.cpp/OpenAI-compatible gateway)
Not For
- • Production-grade enterprise search requiring strong authz/authn, audit logging, and multi-tenant isolation (not described)
- • Use cases needing rich APIs beyond simple top-k snippet search (only one MCP tool is documented)
- • Highly regulated environments without clarity on data handling and retention of embeddings/vector store
Interface
Authentication
README does not describe any auth mechanism protecting the MCP HTTP endpoint; assume none unless the underlying MCP server library provides it (not documented here).
Pricing
The project itself is open-source (MIT) per repository metadata; running costs are external to the package (embedding backend + compute).
Agent Metadata
Known Gotchas
- ⚠ Only one MCP tool (`search_snippet`) is documented; there is no pagination/streaming mechanism described beyond `MAX_RESULTS`.
- ⚠ Indexing behavior (first run vs updates) is not specified; changing snippets may require manual restart/reprocessing.
- ⚠ No auth or rate limit behavior is documented; if you expose the HTTP port broadly, agents may hit failures or overload without guidance.
Alternatives
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Scores are editorial opinions as of 2026-04-04.