"Sumo Logic" matching MCP servers:

Enables AI agents to interact with Eclipse SUMO traffic simulations, automating tasks from data acquisition to signal optimization.

Enables formal logical reasoning, mathematical problem-solving, and proof construction across 11 logic systems including propositional, predicate, modal, fuzzy, and probabilistic logic. Integrates external solvers (Z3, ProbLog, Clingo) for advanced reasoning, with support for proof storage, argument scoring, and cross-system translation.

MCP-Logic is a server that provides AI systems with automated reasoning capabilities, enabling logical theorem proving and model verification using Prover9/Mace4 through a clean MCP interface.

Connects LLMs to Eclipse SUMO traffic simulation, enabling AI agents to automate traffic network generation, demand modeling, signal optimization, simulation execution, and real-time TraCI control through natural language.

Executes advanced logic primitives and cognitive operations via MCP tools, with dynamic LLM configuration and database tracing for reasoning tasks.

Enables AI agents to perform dynamic and reflective problem-solving through a chain of thoughts, allowing them to break down complex problems, revise past thoughts, and explore logic branches before reaching a conclusion.

Production-grade MCP server providing deterministic evaluation, AST sandboxed execution, DNS routing audit, and runtime guardrails for autonomous LLM agents.

An MCP server that exposes vehicle-diagnostic and CAN-bus domain logic as agent tools, with a LangGraph orchestration layer and a human-in-the-loop eval harness.

Provides code-aware context compression by stripping comments, docstrings, and whitespace while maintaining full logic fidelity for AI agents. It features tools for architectural mapping, symbol searching, and token-budgeted multi-file reading.

SWI-Prolog execution for LLMs with CLP(FD) and recursion — boosts logic/constraint accuracy from 73% to 90% on a 30-problem benchmark.

A best-effort universal logic and numerical solver interface using MCP that implements the 'LLM sandwich' model to process queries, call dedicated solvers (ortools, cvxpy, z3), and verbalize results.

Enables AI agents to act on live business objects under enforceable per-call identity, per-tool grants, and mandatory human approval for irreversible actions, with connectors isolated from core logic.

A Node.js MCP server that wraps the Claude Code CLI to enable parallel execution of AI requests for code generation, editing, and refactoring. It features built-in retry logic, model selection, and JSON validation for structured outputs.

BlueMouse is the "Prefrontal Cortex" for LLMs. It uses a 180k+ failure pattern database to validate code logic before execution, acting as a rigorous Quality Gate to prevent hallucinations and unsafe operations.

An MCP server that adds declarative decision trees to FastMCP, enabling deterministic branching logic for runbooks and triage flows with a full audit trail.

Provides symbolic reasoning capabilities by converting natural language logical problems into Answer Set Programming (ASP) format and solving them using the Clingo solver. Enables users to perform formal logical reasoning, verify logical arguments, and get step-by-step explanations for complex logical problems.

Enables AI coding agents to execute formal, stateful workflows with typed contracts, postcondition enforcement, and structured retry logic.

Brings rubber duck debugging to AI-powered IDEs by providing a tool for articulating problems and clarifying logic in natural language. It helps developers and AI agents reveal hidden assumptions and surface solutions through structured self-explanation and reflection.

An MCP server for the Pyke logic programming engine that enables LLMs to perform logical reasoning using knowledge bases with facts, rules, and queries. It supports session management, forward chaining inference, and bulk loading of programs in Logic-LLM format.

MCP server that exposes the DALI2-Agent-Brain symbolic verification system as tools, allowing MCP clients to submit reasoning problems for formal Prolog-based verification.