MCP servers that give coding agents real developer tooling: linters, package registries, test runners, language docs and local build commands. Bu kategoride 13.985 server.
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Connects coding agents like Claude Code and Cursor to an OpenArtifact registry to manage dependencies, find artifacts, and configure package managers.
Diagnose Model Context Protocol servers using transport, initialization, ping, and tool listing checks via stdio or SSE.
Compares the structured content of saved MCP tool call results against a defined shape using success-only or always-on validation policies.
Automates packaging and publishing Python-based MCP servers to PyPI and the Official Registry.
Transforms OpenAPI specs, scripts, CLI tools, and databases into secure, federated MCP endpoints with sandboxing and RBAC.
Integrates Claude Opus 5.5 and Claude Code into Codex Desktop, providing model routing, task delegation to GPU agents, and automated configuration management.
Allows AI coding agents to lookup, search, and read source-quoted technology research to reduce documentation re-reading and token usage.
Provides a security and governance layer for AI agents to control identity, policy, approvals, and threat detection for tools and MCP servers.
Provides a comprehensive course to design, build, test, and deploy AI agents using Python and real-world tools.
Exposes a secure to-do list via MCP tools for use with LangChain agents, FastMCP clients, and IDEs like VS Code.
Exposes multiple MCP servers through a single code API, allowing AI clients to execute sandboxed JavaScript to chain tools and reduce context usage.
Provides test vectors and reference implementations to verify MCP tool definition pinning and compliance with RFC 8785.
Performs static security triage on MCP server configurations to identify secrets, dangerous tool exposures, and prompt injection risks.
Provides a Unity 6 starter kit that enables AI agents to control the Unity Editor and Blender via MCP for automated game development.
Manages the lifecycle of local MCP servers on Windows by cleaning up idle backends and monitoring resource usage.
Provides a lifecycle toolkit and pre-flight checklist for building, testing, and publishing MCP servers.
Integrates committed work from multiple AI coding agents through a local Git merge queue with automated validation and durable receipts.
Provides a first-principles engineering guide to AI mechanisms like embeddings, RAG, and MCP using standard-library Python.
Allows an AI agent to explore GitHub repositories, inspect issues, pull requests, and repository activity through conversational queries.
Provides a real-time telemetry dashboard and terminal visualizer to monitor tool mesh routing, token economies, and MCP server health.
