"Elm" matching MCP servers:

Enables AI assistants to interact with IBM Engineering Lifecycle Management (ELM), including DOORS Next Generation, EWM, and ETM, for managing requirements, work items, tests, and project builds through natural language.

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

Enables AI agents to interact with HP ALM / Quality Center for QA workflows including test case management, test execution, defect tracking, and requirements management.

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

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

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 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 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.

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.

MongoDB-integrated MCP server for documenting and analyzing LLM safety challenges in the Grey Swan Arena competitions.

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.

Exposes llm-behave's behavioral regression testing as MCP tools, allowing offline semantic similarity checks on LLM outputs without any API calls.