"Project dependency resolution and analysis" matching MCP servers:

MCP server providing diagnostic tools to analyze software architecture, security, REST API compliance, and dependencies for multiple programming languages. Enables AI agents to run scans and audits on codebases.

Manages project standards, configurations, and API debugging for AI-assisted development, ensuring unified development practices across teams and machines.

Enables LLMs to interact directly with generated REST APIs by exposing CRUD actions as MCP tools and read-only data as MCP resources.

Enables LLMs to discover and use generated REST API services as Model Context Protocol tools and resources, supporting CRUD actions and read-only data access without custom glue code.

An intelligent MCP server for QA automation that enables AI agents to perform stateful API testing by converting Postman collections into MCP tools, with automatic authentication, session-based state management, and transparent error handling.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Validates OpenAPI specs from files, inline content, or URLs, and generates prompts for agent developers based on the API analysis.

A Model Context Protocol (MCP) server for authenticated REST APIs. Drop it into any project, point it at your API, and let AI agents call endpoints — with auto-login, 2FA support, Swagger spec fetch, and fuzzy endpoint search — all without writing a single line of auth code.

Enables searching YApi interfaces by keyword and retrieving detailed interface schemas, including request/response, for project-level tokens.

Provides OpenAPI validation and analysis tools to assist developers in building Model Context Protocol servers. It generates developer context and specific agent prompts based on API specifications to streamline the creation of agentic workflows.

A local MCP server that lets clients discover, inspect, and execute Bruno OpenCollection API collections, while delegating request execution, authentication, scripting, assertions, and environment resolution to the Bruno CLI.

A comprehensive suite of HTTP and API testing tools that enables AI agents to perform requests, check endpoint health, and decode JWTs. It also provides utilities for URL parsing and detailed security analysis of HTTP response headers.

Enables interaction with AIApp BaaS authentication system through keyword-based document search and automatic generation of framework-specific client code. Supports React, Next.js, Vue, and Vanilla JS with TypeScript integration and automatic project ID injection.

A Model Context Protocol server that enables LLMs to interact with Plane.so, allowing them to manage projects and issues through Plane's API for streamlined project management workflows.

A CDP-native MCP server that lets AI agents drive a real Chrome browser with deep network/console inspection, robust page interaction, and the ability to reverse-engineer a site's API flow into runnable code with dependency chaining and replay verification.

Provides tools to communicate with a service dependency API, enabling users to query and interact with service relationship data and dependency mappings.

Exposes ProxyPin's local network capture history to MCP-capable clients for searching, analyzing, and detailing captured HTTP traffic. It enables users to perform API structure analysis and generate client-side code directly from captured requests.

An MCP server that allows users to query and retrieve request and response JSON schemas directly from Swagger/OpenAPI specifications. It supports automatic reference resolution and path parameter matching to help AI models interact with API interfaces.