Provides access to a positional graph knowledge base in Neo4j, with nodes for positions (FEN), edges for played moves ([:PLAYED]), and theory links ([:THEORY]), enabling GraphRAG-based chess analysis and semantic coaching.
Provides SQL access to chess game telemetry in PostgreSQL, including game type, ELO, results, ACPL, and blunder percentages, to support data-driven chess analysis.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ChessGraph-MCP Evaluate 1.e4 e5 2.Nf3 and tell me if Nc6 is playable."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Building a chess Agentic setup that bridges the gap between deterministic game-tree calculation and semantic coaching. Permit the agent to access on stockfish and additional graph knowledge to help it understand what moves are playable on differents positions.
[Chess.com PGN Archives] [Hugging Face Datasets (Parquet/CSV)]
(1500+ ELO Games) (ECO Openings & Endgame Theory)
│ │
└──────────────┬──────────────────────┘
▼
┌───────────────────────┐
│ ETL & Normalizer │ (Python / Polars / python-chess)
└───────────┬───────────┘
│
┌───────────────┴───────────────┐
▼ ▼
┌───────────────────────┐ ┌───────────────────────┐
│ PostgreSQL │ │ Neo4j │
│ Game Telemetry & SQL │ │ Positional GraphRAG │
│ • Game Type │ │ • (:Position {fen}) │
│ • ELO & results │ │ • [:PLAYED] edges │
│ • ACPL / Blunder % │ │ • [:THEORY] links │
└───────────┬───────────┘ └───────────┬───────────┘
│ │
└───────────────┬───────────────┘
▼
┌───────────────────────┐ ┌───────────────────────┐
│ Hermes-Agent │◄─────►│ Stockfish MCP Server │
│ Orchestration Layer │ MCP │ • MultiPV Eval │
│ (Cypher + SQL + MCP) │ │ • WDL Probabilities │
└───────────────────────┘ │ • Positional Motifs │
└───────────────────────┘This server cannot be deployed
Analytical memory for AI agents: a real Postgres queried in plain English over MCP. One command.
Shared memory for AI agents, as a graph in your own Postgres. Writes never call an LLM.
Knowledge graph for AI agents. Query concepts, walk edges, get advisories.
Supabase-compatible Postgres for agents: projects, branches, SQL, auth, storage, email.