"Deutsche Post" matching MCP servers:

A remote MCP server exposing 3 tools via Model Context Protocol: score_prompt (free, grades any LLM prompt A-F on 0-40 scale), optimize_prompt (returns optimized prompt + dimension breakdown, $0.025 USDC), and compare_models (Claude vs GPT-4o head-to-head, $1.25 USDC). Remote HTTP server at /api/mcp.

Enables agents to check whether an execution trace contains the evidence needed for post-hoc causal reconstruction, retrieve individual requirement details, and list evidence requirements per failure class over MCP stdio. It works on files, directories, or inline JSON locally and never transmits or persists data.

Bug bounty platform for vibe coders. Post bugs from AI-generated code, browse bounties, apply to fix, submit solutions, and run website diagnostics from any MCP client.

An MCP server that lets an agent verify its own extracted data against a policy, returning per-field dispositions (post, review, or escaped) to catch confidently wrong answers.

Post-generation quality gate MCP server that audits and fixes AI-generated code for UI/UX design compliance and backend architecture robustness via tools like critique_ui, critique_backend, and generate_fix.

Integrates Slack channels and Slack Lists to automate bug report scanning and QA status management. It enables users to retrieve bug details, update list items, and post fix updates directly within Slack.

Enables HTTP API testing (GET, POST, PUT, DELETE) via the Model Context Protocol, powered by Playwright.

Membrane is an MCP server that combines DORA metrics, deploy checks, and gamification rewards into a post-deploy verification layer for AI agents.

Creates disposable remote MCP servers and records only the protocol boundaries they observe, enabling safe receipts of how far managed hosts like Claude, ChatGPT, or Cursor actually progressed.

Enables MCP clients and LLM agents to fetch pull-request diffs, map changed files to tests, run affected tests in a sandboxed workspace, and post review comments or labels to GitHub, with dry-run and fixture demo modes.

An MCP server that provides pre-flight validation and post-processing tools for OpenMC Monte Carlo transport simulations, catching common authoring mistakes before jobs hit the HPC queue.

Enables testing and interacting with any REST API endpoints directly from MCP clients such as Claude and Cline, supporting GET, POST, PUT, DELETE, and PATCH requests with detailed status, header, and body responses. It handles Basic, Bearer, and API Key authentication, custom headers, configurable SSL verification, and response size limits.

Enables AI agents to drive SECS/GEM host testing by connecting to equipment, sending SECS messages, running E30 startup conformance reports, and monitoring events and status.

Exposes an agent-based simulation of post-scarcity governance as callable tools for AI agents, including simulation runs, compliance checks, and test execution.

Enables sweeping commission and slippage assumptions across backtest results to see how performance degrades, pinpoint the exact cost level where a strategy's edge hits zero, and audit for look-ahead-bias patterns like data leakage and repainting indicators.

Enables users to test and interact with any REST API endpoints directly from their development environment through MCP clients such as Claude and Cline. Supports GET, POST, PUT, DELETE, and PATCH requests with configurable authentication, custom headers, SSL verification, and response size limits.

Enables testing and interacting with REST API endpoints from Claude, Cline, and other MCP clients, supporting GET, POST, PUT, DELETE, and PATCH requests with authentication, custom headers, SSL control, and response size limits.

Enables measuring how the same MCP tools implemented in FastMCP, the official MCP Python SDK, and fastapi-mcp differ in token overhead, schema fidelity, and call latency.

Enables testing and interacting with any REST API endpoint directly from MCP clients like Claude and Cline, supporting GET, POST, PUT, DELETE and PATCH requests with detailed response information, custom headers, and Basic, Bearer or API key authentication.

Enables MCP-connected AI agents to review GitHub pull requests and post meaningful inline review comments directly on them through natural language prompts.