Neo Mcp Logic Analyze

What is neo-mcp-logic-analyze?

neo-mcp-logic-analyze is a Python MCP server that provides controlled logic analysis from natural-language statements and arguments. It emphasizes auditable output and teaching-oriented explanations, making it suitable for educators, students, and developers who need structured logical formalization, ambiguity detection, consistency/entailment checking, and counterexample generation.

How to use neo-mcp-logic-analyze?

Install the package with pip install . after cloning the repository. The server is designed to be launched by an MCP client (e.g., Claude Desktop, Cursor) over stdio. Configure the client with the command neo-mcp-logic-analyze. Then invoke the exposed tools, resources, or prompts from your MCP host.

Key features of neo-mcp-logic-analyze

• Tool nl_parse_logic for structured formalization into propositional or first-order logic.
• Ambiguity detection via detect_ambiguities (e.g., quantifier‑scope issues).
• Consistency checking (check_consistency) with unsat core support.
• Entailment checking (check_entailment) with proof sketches.
• Counterexample search (find_counterexample) when entailment fails.
• Teaching‑oriented prompts (teach_logic_step_by_step, review_formalization).

Use cases of neo-mcp-logic-analyze

• Automatically formalize natural‑language arguments for logic exercises or automated reasoning pipelines.
• Help students understand logical structure by explaining formalization choices step‑by‑step.
• Detect ambiguous phrasing in statements before rigorous analysis.
• Verify the logical validity of short arguments (e.g., “If A then B; A, therefore B”).
• Generate counterexamples to illustrate invalid inferences in classroom settings.

FAQ from neo-mcp-logic-analyze

What runtime does neo-mcp-logic-analyze require?

Python 3.11 or later.

How is the server launched?

It is launched by an MCP client over stdio. The command is neo-mcp-logic-analyze (the installed package entry point). The process waits for a client connection until interrupted.

Can this server handle long or complex texts?

No. The project is optimized for short inputs; natural‑language interpretation is heuristic and intentionally restricted. Long free‑form texts are not supported.

What types of logic are supported?

Propositional logic and a restricted fragment of first‑order logic (FOL).

Does the server provide explanations for its outputs?

Yes. The explain_formalization tool and the teach_logic_step_by_step prompt offer natural‑language explanations of the formalization process.