mcp-rag
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | No | API key for OpenAI (required if any backend is openai) | |
| VOYAGE_API_KEY | No | API key for Voyage AI (required if embedding backend is voyage) | |
| ANTHROPIC_API_KEY | No | API key for Anthropic (required if generator backend is anthropic) | |
| MCPRAG_EMBEDDING_BACKEND | No | Embedding backend (local, voyage, openai, sentence-transformers) | local |
| MCPRAG_GENERATOR_BACKEND | No | Generator backend (local, anthropic, openai) | local |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_documentsB | Search the knowledge base for passages relevant to a query. Args: query: natural-language search query. top_k: number of passages to return. method: retrieval strategy — "semantic", "lexical", or "hybrid". |
| answer_questionA | Answer a question grounded in the knowledge base, with citations. Args: question: the user's question. top_k: how many passages to ground the answer in. |
| list_sourcesA | List the documents available in the knowledge base. |
| get_statsA | Return index size and the active embedding/generation backends. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clear, distinct purpose: answer_question for generating answers, search_documents for retrieving passages, list_sources for enumerating documents, and get_stats for server metadata. No functional overlap.
All tool names follow a consistent verb_noun pattern in snake_case (answer_question, get_stats, list_sources, search_documents), making the API predictable.
With 4 tools, the server is well-scoped for its RAG purpose. Each tool covers a core operation (search, answer, list sources, stats) without unnecessary bloat.
The tool surface covers all essential tasks for a knowledge base query system: searching, answering with citations, listing available documents, and retrieving system stats. No obvious gaps for the stated purpose.