python-mcp-server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ENV | No | Environment selection (default: local) | local |
| NEO4J_PASSWORD | Yes | Password for Neo4j database | |
| OPENAI_API_KEY | Yes | OpenAI API key for embeddings | |
| POSTGRES_PASSWORD | Yes | Password for PostgreSQL database |
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_knowledgeA | Search factual knowledge in the Graphiti knowledge graph. USE WHEN: You need verified facts, entities, relationships, or structured knowledge. Combines semantic search, BM25, and graph traversal. |
| rag_searchA | Search documents using vector similarity for context. USE WHEN: You need detailed context, explanations, or source documents. The query string is embedded internally; no pre-computed vector needed. |
| verify_factA | Retrieve knowledge-graph evidence relevant to a statement. USE WHEN: You want to check a claim against the graph. Returns related facts; the caller judges whether they support or contradict the statement. No boolean verdict — entailment is the caller's job. |
| combined_searchA | Search both the knowledge graph and document vectors. USE WHEN: You need both structured facts and supporting context. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| answer_with_verification | Template for answering with evidence-grounded verification. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| knowledge_instructions | How to answer accurately using this knowledge base. |
| example_queries | Example patterns for effective knowledge retrieval. |
TDQS
Scored across 4 tools
Each tool has a reasonably clear purpose: graph knowledge search, document vector search, combined search, and claim verification. The only mild overlap is between search_knowledge and verify_fact, but the descriptions distinguish general exploration from targeted claim checking.
Names are all lowercase snake_case and readable, but the pattern is inconsistent: search_knowledge and verify_fact are verb_noun, while rag_search and combined_search are qualifier_search. This makes the naming convention less predictable across the set.
Four tools is a well-scoped size for a retrieval-focused server. Each tool has a distinct role, and combined_search earns a place as a convenience for workflows needing both knowledge graph and document context.
For a read-only search and verification server, the core operations are covered: graph search, RAG search, combined search, and claim verification. Missing ingestion or update/delete tools would be a gap only if the server were meant to manage the underlying knowledge store, which the descriptions do not indicate.