"Methods for Analyzing Code in a Project" matching MCP servers:

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

Enables exploring Microsoft Fabric / Power BI workspaces and semantic models, and executing ad-hoc DAX queries.

An MCP server that scaffolds full-stack projects with consistent structure, Docker setup, CI/CD pipelines, and database configuration.

An MCP-powered AI agent that audits your Gmail for recurring subscriptions, detects silent price increases, and flags unused services.

Enables AI agents to control NetEase Cloud Music playback, login, search, and get personalized recommendations through official APIs.

A minimal MCP server template that offers calculator tools, resources, and prompts for learning NitroStack fundamentals, built with TypeScript and Zod.

Provides CodeQL language intelligence to AI agents via MCP, enabling completions, hover, definitions, references, diagnostics, formatting, and in-memory file updates for QL code.

Bridges a local AI agent to a Colab session in the browser, enabling code execution and file operations through the Model Context Protocol.

A comprehensive development toolkit with 23 tools covering code quality analysis, development efficiency, and project management. Enables AI-assisted code review, test generation, performance analysis, SQL generation, UI component creation, and automated project documentation.

A Model Context Protocol server that provides programmatic APIs for running mutation tests with mutmut, analyzing results, and improving test coverage in Python projects.

A template and demonstration project for building, testing, and deploying remote MCP servers using FastMCP and uv. It provides a foundational structure for creating MCP-compliant tools that can be hosted publicly and integrated with LLM agents.

Enables interaction with openGauss databases through multiple transport methods (Stdio, SSE, Streamable-Http). Supports database operations and queries with configurable connection parameters.

Enables code execution in 70+ programming languages and databases through the Onecompiler API, allowing users to run and test code directly from their MCP-enabled applications.

Enables AI assistants to manage personal finances by storing, analyzing, and exporting expense data using a persistent PostgreSQL database. Supports adding/editing expenses, generating spending summaries, detecting top categories, and creating monthly reports.

A personal memory engine and MCP server that stores durable facts in markdown files managed via git, enabling hybrid search (lexical + semantic) through an MCP interface for persistent context across LLM sessions.

Enables querying git commit history to analyze when and why code changes happened, providing authorship context and diffs for specific modules.

Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.

Read-only MCP server for the Bucharest Stock Exchange, exposing instrument data, OHLCV candles, and fundamentals through a public backend with no authentication.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Enables AI assistants to query, analyze, and discover FileMaker database schemas via OData v4, with pandas-powered analytics for efficient data summaries.