MCP servers that give coding agents real developer tooling: linters, package registries, test runners, language docs and local build commands. 13.985 servers in dieser Kategorie.
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Provides source-grounded knowledge about the Formly library to coding agents with curated facts, citations, and evidence.
Performs local consistency checks on OAuth test assertions and release identifiers to find missing or inconsistent evidence.
Provides local project memory for AI coding agents by indexing code, Git history, and repository discussions with cited context.
Enables Claude Desktop's Cowork to read and write files within a WSL environment via a local MCP server.
Reclaims disk space by safely deleting disposable AI agent artifacts, build outputs, and tool caches on macOS.
Provides a comprehensive modding toolkit for Islet Online, including a world editor, resource editor, and an MCP server for AI agents.
Identifies Python dependencies lacking compatible binary wheels for specific Python versions and platforms, suggesting the necessary version bumps.
Provides AI agents with typed operations for cipher decoding, symbolic constraints, automata, and exact candidate checks.
An OWL ontology linter that checks for modeling pitfalls, metadata issues, logical consistency, and SHACL validation with Korean language reports.
Grades a domain's email authentication setup by analyzing public DNS records for SPF, DKIM, DMARC, and other security protocols.
Allows AI agents to fill predefined fixed-width slots in files while proving that no other content was modified.
Tests LLM judges for position and padding bias and converts conversation logs into reusable evaluation suites.
Identifies bottlenecks in machine learning training runs, applies optimizations, and verifies speedups without altering the loss trajectory.
Converts Jupyter notebooks into verified Python pipelines by locking outputs and using AI agents to refactor code without changing results.
Scans legacy Python 2 and Django codebases to generate prioritized, phased migration plans for Python 3 upgrades.
A collection of training resources and implementation examples for building, deploying, and consuming Model Context Protocol servers.
Provides LLM-friendly API cards and a static checker for CuPy and nvmath-python to prevent hallucinations in GPU math code.
Enables AI agents to capture, read, and triage bug reports including annotated screenshots and screen recordings stored locally.
Diagnose OAuth discovery and authentication failures on remote MCP servers by simulating a conforming client step-by-step.
Records AI agent steps and uses replay experiments to diagnose and prove the root cause of failures with zero model calls.
