"Sumo Logic" matching MCP servers:

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.

Gives an AI agent complete knowledge of a specific DevExpress XAF application: its entities, controllers, actions, business rules, Model Editor customizations, custom property editors and the version-gated data migrations that ran once on a production database. Reads the source with Roslyn without compiling it, so it works on a branch that does not build and needs no DevExpress licence.

Enables efficient code navigation and retrieval through natural language search, BM25 ranking, and fuzzy matching across multiple programming languages. It drastically reduces token usage by allowing Claude to query specific code symbols and logic instead of reading entire files.

Lets AI agents and users analyze Mitsubishi MELSEC .gx3 projects directly, tracing coil conditions, checking device references, comparing program changes, and inspecting ladder logic without opening GX Works3.

Analyzes GitHub Pull Request diffs for security vulnerabilities and data leaks, using a deterministic filter to skip trivial changes and an AI-powered analysis for logic changes.

Chrome browser JS reverse engineering MCP tool for CTF/Web security, enabling dynamic analysis and restoration of login encryption logic.

An MCP server for ABB/Hitachi Energy PCM600 that provides offline IEC 61850 SCL file analysis, .apcmp project archive inspection, ACT logic recovery, and safe read-only drive of the PCM600 CLI for project exports and validation.

An MCP server that brings senior-QA discipline to AI coding assistants, enabling test planning, TDD, mutation testing, and code review.

Hybrid code quality analysis server combining fast logic-based checks with AI-powered deep analysis, offering tools for summaries, quick wins, and trend tracking.

Enables deterministic verification for AI assistants by executing Python code that uses symbolic engines like SymPy and Z3 for math, logic, and code analysis.

Connects LLMs to GitHub and GitLab to analyze pull and merge requests for logic, security, and architectural alignment. It provides tools for fetching diffs, file contents, and project metadata, alongside guided prompts for professional code reviews.

Enables AI agents to read and understand local Mendix project structure and logic by connecting directly to the .mpr file via MCP. Allows querying microflows, entities, attributes, and modules in read-only mode without requiring cloud access.

SourceSage is an MCP (Model Context Protocol) server that efficiently memorizes key aspects of a codebase—logic, style, and standards—while allowing dynamic updates and fast retrieval. It's designed to be language-agnostic, leveraging the LLM's understanding of code across multiple languages.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

SourceSage is an MCP server that efficiently memorizes key aspects of a codebase—logic, style, and standards—while allowing dynamic updates and fast retrieval.

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.

Analyzes Hytale game SDK by automatically detecting the installation, decompiling server logic, and exposing an AI-ready interface via the Model Context Protocol for searching API and assets.

MCP server that gives LLMs access to formal verification via Z3 and SWI-Prolog, plus tree-sitter-based source code analysis. Translates natural language problems into formal logic using a template-based pipeline, verifies results with mathematical certainty, and analyzes call graphs for reachability, dead code, and impact analysis.

Enables any debugger to become a HostProto host by supplying an EngineBinding, exposing revision-scoped debugging semantics through an MCP surface. It computes targets, preconditions, receipts, recovery, and earned capabilities without requiring adapter-specific 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.