"Common Lisp" matching MCP servers:

Enables users to import scientific figures together with their code, review and publish them as immutable releases into one user-chosen local library, then search, preview and materialize an exact confirmed template into any project from Claude Science, Cursor, Codex or other stdio MCP hosts. It keeps the catalog on disk and never executes plotting code, so the host agent inspects files while the server hashes, versions, gates and publishes them.

Provides MCP servers that wrap common developer tools (git, npm, docker, etc.) returning structured JSON output, enabling AI agents to reliably interact with these tools without parsing fragile terminal text.

Enables AI agents to interact with common developer tools such as Git, npm, Docker, and test runners by returning schema-validated JSON instead of raw terminal text. This reduces brittle parsing errors and token usage while providing typed data agents can act on directly.

A unified MCP server with composable tools for GitHub operations, file management, shell execution, kanban boards, Discord messaging, and package management. Features role-based security, HTTP/stdio transports, and a web-based development UI.

Enables AI code review to query semantic LSP context such as hover and definition against a specific Git commit or candidate, with verifiable receipts for the exact code state under review.

Enables AI agents to invoke common developer tools — git, GitHub, npm, Python, cargo, Docker, Kubernetes, linters, and test runners — and receive schema-validated structured JSON instead of raw terminal text. This removes brittle string parsing and cuts token usage dramatically on verbose commands like builds, installs, and test runs.

. A good description should be concise and describe what the server enables users to do, e.g. * Enables to interact with WordPress sites...' Then a huge block? Actually the user message includes the README content inside tags, then after the readme, there is a large block of text starting with '{

Enables AI agents to invoke common developer tools such as git, npm, Docker, and test runners and receive clean, schema-validated JSON instead of fragile terminal text, reducing token usage and parsing errors.

A utility that helps diagnose and fix GitHub Actions workflow failures by analyzing run logs, identifying common failure patterns, and suggesting specific fixes through a structured decision tree.