rag-mcp-server
Provides an MCP server that builds and queries a local Retrieval-Augmented Generation (RAG) knowledge base from document directories. It extracts text from .txt and .pdf files, chunks content, computes embeddings (SentenceTransformers), indexes vectors with FAISS, and stores document metadata in SQLite; exposes MCP tools for initializing, searching, refreshing, getting stats, and listing documents.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Server is local/file-based and does not describe authentication, encryption in transit (no network API shown), or authorization controls. It processes PDFs with PyMuPDF and embeds text via SentenceTransformers; risks largely relate to local filesystem access, data exposure through retrieved content, and supply-chain/dependency management (no CVE/security posture described).
⚡ Reliability
Best When
You want an offline/local MCP integration to index and retrieve from a local document corpus using an AI agent.
Avoid When
You need strong authentication/authorization, network-accessed service security, or cloud-managed guarantees (SLA/compliance, audit trails).
Use Cases
- • Local RAG over a folder of documents (semantic search + retrieval)
- • Building per-project knowledge bases for an IDE agent via MCP tools
- • Incremental updates when documents change (refresh knowledge base)
- • Document exploration workflows (list documents, view stats, search with top-k results)
Not For
- • Serving multi-tenant or internet-facing production workloads without additional hardening
- • Use cases requiring fine-grained access control across users/tenants
- • Use cases needing remote managed vector DBs or hosted APIs (this is local/file-based)
- • Highly regulated environments without explicit data-handling/compliance assurances
Interface
Authentication
No authentication/authorization mechanisms are described; the server appears intended for local use via MCP client configuration and operates on local filesystem paths.
Pricing
Open-source (MIT).
Agent Metadata
Known Gotchas
- ⚠ No explicit guidance on concurrency (multiple agents/processes updating the same knowledge base)
- ⚠ Tool behavior around missing/empty knowledge bases (e.g., calling semantic_search before initialize) isn’t documented in detail
- ⚠ No documented rate limiting for tool calls (agents may need to implement backoff themselves)
- ⚠ Large PDFs may be slow; progress bars exist but agent-facing guidance for long-running operations is not specified
Alternatives
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Scores are editorial opinions as of 2026-04-04.