"Information about Gemini code" matching MCP servers:

Connects Claude Code with Google's Gemini AI, allowing users to ask Gemini questions, get code reviews, and brainstorm ideas directly within Claude Code.

Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

Enables AI assistants to browse and interact with both Gopher and Gemini protocol resources safely and efficiently.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

Enables developers and AI agents to compress context, plan token budgets, rerank results, patch code, analyze ASTs and dependencies, isolate stack traces, compile polyglot types, solve deterministic math, schedule constraints, judge A/B tests, and diff agent memory checkpoints through 12 specialized tools.

High-performance Model Context Protocol (MCP) server connecting Claude Code and AI agents to Google Gemini 3.8 Flash, Gemini 3.1 Pro, reasoning effort, and image generation.

An enhanced sequential thinking tool optimized for programming tasks that helps break down complex coding problems into structured, self-auditing thought steps with branching and revision capabilities.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.

Gemini-powered MCP server for automated code review, analysis, and documentation.

MCP server for GitHub code retrieval and reuse, using SQLite+FTS5 indexing and search history to enable search-first, requirements-refined code search from GitHub repositories.

An MCP server that exposes Gemini's Deep Research Agent for comprehensive web research.

Checks a code repository without running it: Safe, Works and Complete scores with an A-F grade, catching leaked keys, dangerous calls, invented or lookalike packages, broken imports and missing project files. Standard-library Python; MCP tools scan, orphans and explain.

A read-only MCP server that gives LLM agents deterministic operational facts about Trello boards, including structural history, card movement, workflow flow, staleness, and due-date information.

Enables coding agents and CI/CD pipelines to audit untrusted code by detecting Python sandbox escapes, SQL injection taint, ReDoS regex vulnerabilities, and CORS misconfigurations, while generating Seccomp BPF filters for safer execution.

Semantic code search for any MCP client: indexes local folders of code and docs and answers plain-language or code queries with matching passages, file paths, line ranges and scores. Embedding runs on the local CPU with the Motherlode code-search encoder, so no file content leaves the machine; the encoder downloads once from Hugging Face. Install: npx -y blackwindow-weave-mcp.

An MCP server that gives orchestrator agents fine-grained control over interactive Claude Code sessions running inside tmux, enabling mid-session steering, interruption, and token-efficient result extraction.

Self-hosted semantic code search for AI coding agents. Indexes many repositories at once with tree-sitter AST chunking, local llama.cpp embeddings and Qdrant, so Claude Code, Cursor, Codex and any MCP client can find code by meaning, trace usages and callers, and read the git history behind each chunk. No API keys, and your code never leaves your machine.

Persistent project context for Google Gemini. 12 MCP tools for .faf Project DNA — auto-detect your stack, validate, score, and sync across CLAUDE.md, GEMINI.md, and AGENTS.md. Python/FastMCP. IANA-registered format (application/vnd.faf+yaml). 183 tests. One file, every AI platform.

A TypeScript MCP server that enables agents to request focused, one-shot consultations with allowlisted Gemini models, with guaranteed model provenance and no silent model changes.

Allows LLMs to execute Python code in a specified Conda environment, enabling access to necessary libraries and dependencies for efficient code execution.

Concord MCP allows Claude Code, Codex, Cursor, Grok Build and Gemini to message each other in real time.

Provides access to the SonoVault music metadata API, enabling search and retrieval of information about 90M+ tracks, artists, labels, and releases, including ISRC, ISWC, and cross-platform IDs.

This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

One workspace for every AI coding assistant. Governance tools for API lint, diff, persistent ledger, multi-model deliberation, security audit, and test verification. Works with Claude Code, Codex, Cursor, and Gemini CLI.

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

A persistent, Git-versioned map of your whole codebase and database schema that coding agents read before they touch anything. Local-first MCP server + CLI in Go: a governed repository index of code knowledge that gives Claude Code, Codex, Cursor, and opencode long-term context, memory, and code intelligence.

Exposes Lachesis's code navigation tools over MCP, letting LLM agents ask precise compiler-level questions about dataflow, taint, callers, and guards in source code.

Provides access to Shodan API functionality, enabling AI assistants to query information about internet-connected devices for cybersecurity research and threat intelligence.