Stockfish
MCP server implementation by sonirico that provides AI assistants with direct access to the Stockfish chess engine through UCI (Universal Chess Interface) protocol commands. Built in Go with both persistent and ephemeral session management, the server supports standard UCI commands like position setup, move analysis, engine configuration, and search operations with configurable depth, time limits, and evaluation parameters. The implementation offers flexible deployment options including Docker containerization and HTTP/stdio transport modes, with comprehensive session lifecycle management, command validation, and timeout handling. Designed for AI applications requiring chess analysis capabilities, game evaluation workflows, move suggestion systems, and educational chess tools where programmatic access to world-class chess engine analysis is needed without managing complex UCI protocol interactions directly.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- cloud-infra
- Source
- PulseMCP
- Repository
- github.com/sonirico/mcp-stockfish
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Stockfish from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.