"An application or tool called 飞书 (Feishu)" matching MCP servers:

Enables AI assistants to interact with Gitea repositories through intelligent tools for issue/PR management, workflow analysis, compliance checking, and content generation, plus 200+ CLI commands for complete CRUD operations.

Enables intelligent management of Google Cloud Platform infrastructure using Terragrunt, with support for experimental stacks, parallel deployments, resource visualization, cost analysis, and comprehensive infrastructure operations through natural language.

A multi-Jenkins server management and automation tool that enables intelligent scenario mapping, multi-server management, and complete CI/CD lifecycle operations through natural language interactions.

A feature-complete TypeScript project template for rapidly developing Model Context Protocol tools with integrated best practices for code quality, testing, and automated deployment.

An interactive tool that enables users to benchmark vLLM endpoints through MCP, allowing performance testing of LLM models with customizable parameters.

Audits dbt Core projects for migration blockers and generates actionable guidance for migrating to dbt Cloud, including auto-fixing deprecated syntax.

SSWP MCP — Deterministic software attestation for AI-augmented development. Witness any repo through a 5-gate pipeline, adversarially probe dependencies, and produce self-verifying .sswp.json attestations. Fleet registry across 131 nodes with tamper-proof audit ledger and FTS5 search. Agent-native — one tool call from Hermes, Claude, or Cline.

MCP server with the watch_ci tool that polls GitHub Actions until a commit's CI run finishes, then returns the outcome and failed log tail in one call.

Record an agent's MCP tool-call workflow once, replay it deterministically for zero tokens, and get a receipt — a snapshot-testing and deterministic-execution layer for Agent Skills.

Diffgate MCP server acts as a code review engine, enabling AI coding agents to analyze and validate code diffs before application. It enhances AI workflows by providing self-checking capabilities to optimise and secure code changes.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

MCP connector for AppCrane that serves a bundled tool catalog for offline introspection and proxies tool calls to a self-hosted AppCrane instance for deploying and managing AI-built apps.

Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp

An MCP server that enforces disciplined Git workflows by planning coding tasks into small, test-gated commits, ensuring tests pass before staging and preventing out-of-scope or oversized changes.

Blocks unsafe PostgreSQL migrations before an AI agent writes or runs them. check_before_apply returns a pass/fail gate; reads the real Postgres parser, classifies the lock each statement takes, checks 112 safety rules. Runs offline, no database required.

Checks whether software dependencies (npm packages, GitHub Actions, MCP servers, Docker images) are alive, dormant, abandoned, archived, or deleted using dated observations from lastseen.dev.

MCP server that lets coding agents test AI agents. Create YAML test cases, snapshot golden baselines, check for regressions, and generate visual reports all from inside Claude Code or any MCP-compatible tool. Works with LangGraph, CrewAI, OpenAI, Claude, Mistral, and any HTTP API.

MCP server that automates the publishing and validation of Tableau workbooks and data sources on Tableau Server or Tableau Cloud, enabling an AI agent to discover, build, validate, and publish content without human intervention.

Enables management of Railway.app infrastructure through natural language, including deploying services from GitHub repos or Docker images, managing environment variables, monitoring deployments, and configuring networking and volumes.

Enables AI assistants to query and manage KubeVela application delivery: check application status, trigger deployments, track workflows, manage addons, and more.