"Documentation URL for a service API, data, and LLM usage" matching MCP servers:

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.

Enables LLMs to interact directly with generated REST services by discovering and invoking CRUD tools and reading exposed resources for list and get-by-id data.

MCP server that provides tools for evaluating LLM agent reliability, including adversarial task generation, automated LLM-as-judge assessment, and confidence statistics.

A local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.

Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.

MCP server to mock OpenAI, Anthropic, and Google Gemini responses for testing AI integrations without real API keys or token spend.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables provider-agnostic LLM discovery, capability testing, stability screening, and AIPerf benchmarking through MCP, with persistent SQLite history.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables coding agents to evaluate a single model over a golden set with trap cases, reporting accuracy overall and on traps, mean latency, cost per request, and failing cases. Also compares a baseline against a candidate model and returns a pass/fail gate verdict with failure reasons, so a model swap can be checked before it ships.

Enables local, cross-provider preflight validation of LLM integration changes—model, prompt, structured-output, and provider-call modifications—with no-spend checks, evidence on speed, tokens, and cost, and explicit human approval before paid runs.

MCP server for Jama Connect that brings requirements, traceability, tests, and baselines into MCP clients like Claude, Cursor, VS Code, or GitHub Copilot, with access control, audit trail, and rate limiting. It provides 53 tools across 8 individually enableable toolsets, with an admin dashboard for managing access and usage.

Enables automated WCAG 2.2 AA accessibility audits of Figma designs and webpages. Generates detailed markdown reports with severity-grouped violations, specific criterion references, and concrete fix recommendations.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Turns an OpenAPI spec into a ready-to-run test harness with local LLM agents: WireMock stubs, contract tests, robustness probes, and chaos scenarios you can switch on and off in a running WireMock.

Thin HTTP wrapper for the Generate-Data.com API, enabling data generation, schema proposal, and field type listing through MCP tools.

Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.

Enables Claude Code to benchmark llm-d from plain English, providing 35 tools and 5 workflow prompts for probing clusters, planning, deploying, running benchmarks, and analyzing results via the Model Context Protocol.

Exposes LLM output validation (schema compliance and prompt-injection detection) as an MCP tool, enabling any MCP-compatible agent to apply real-time guardrails without importing the codebase.