"Using another LLM for research purposes" matching MCP servers:

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

Enables evidence-grounded implementation research by searching GitHub, scoring candidate repositories, extracting source evidence, and generating planning artifacts like research and implementation checklists.

MCP server for reviewing code changes using LLMs, supporting Copilot, Ollama, and OpenAI-compatible endpoints.

MCP server connecting Claude Code to LM Studio, delegating token-expensive tasks to a local model while keeping the cloud model in control. It reduces cloud context usage by reading files locally and returning only the processed results.

A stdio MCP server that queries Commonware blockchain research via NotebookLM, enabling users to ask research questions, get code-backed implementation suggestions, search reference repositories, and validate documents against source code.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

MCP server that exposes an AI decompiler via an OpenAI-compatible API, enabling decompilation, explanation, and variable renaming of disassembly for binary analysis.

Bridges local LLMs running in LM Studio with MCP clients like Claude Desktop to perform reasoning and analysis tasks while keeping sensitive data private. It features a suite of tools for local code review, privacy scanning, and content transformation using auto-discovered local models.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

MCP server exposing a shared pr-review skill with tools to list, get, run, and evaluate skills, enabling AI assistants to perform code review tasks.

Processes large files, error logs, and codebase searches locally via Ollama to reduce token consumption in Cursor by up to 80%.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Uses Claude as an independent second-model reviewer inside Codex, performing reviews on disposable Git snapshots with structured findings.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

A privacy-first MCP server that provides local LLM-enhanced tools for code analysis, security scanning, and automated task execution using backends like Ollama and LM Studio. It enables symbol-aware code reviews and workspace exploration while ensuring that all code and analysis remain strictly on your local machine.

MCP server giving Claude (and any MCP-compatible client) live access to SAP systems via ADT for reading, searching, editing, and activating ABAP objects.

MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.

Integrates local language models (like Qwen3-8B) with MCP clients, providing tools for chat, code analysis, text generation, translation, and content summarization using your own hardware.