Logic-LM (Answer Set Programming)
Logic-LM MCP server by Steven Wang that provides AI assistants with symbolic reasoning capabilities through Answer Set Programming (ASP) and the Clingo solver. Built with Python using FastMCP, the implementation follows a three-stage Logic-LM pipeline: translating natural language problems to ASP code, executing symbolic reasoning with Clingo, and interpreting results back to natural language. The server includes comprehensive ASP translation guidelines, template libraries for common logical patterns (syllogisms, conditionals, universal quantification), and tools for direct ASP program verification with self-refinement capabilities. Designed for applications requiring formal logical reasoning, constraint satisfaction problems, and symbolic problem-solving where traditional LLM reasoning may be insufficient, particularly useful for academic research, automated theorem proving, and complex multi-step logical deduction tasks.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/shipitsteven/logic-lm-mcp
- Author type
- human
- Updated
- 2026-04-29
Use via MCP
Resolve Logic-LM (Answer Set Programming) 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.