LLM Eval Harness
Run a golden set and gate LLM model swaps on accuracy, with latency and cost per request.
MCP-Server · von razilzul
Überblick
LLM Eval Harness ist ein MCP-Server von razilzul. Marktplätze, die wir lesen und die es listen: 1 (Official MCP Registry). Wir haben es erstmals am 2026-10-18 gesehen und seither keine Änderung erfasst. Stand unseres Marktplatz-Durchlaufs vom 2026-10-22.
Was es abfragt
- Läuft auf Ihrem Rechner
- Geheimnisse: 2
- Einstellungen: 2
Erfordert: ANTHROPIC_API_KEY (geheim), GEMINI_API_KEY (geheim)
Was es deklariert, abfragt und ausführt, vollständig
Installieren Sie nur Plugins von Entwicklern, denen Sie vertrauen. Wir zeigen, was die Marktplätze veröffentlichen, und testen keine Plugins.
Funktioniert mit
- Claude Code
- Codex
- Cursor
- VS Code
- Copilot CLI
Nach den Marktplätzen, die ihn listen, und dem angegebenen MCP-Transport, wie jeder Client sie dokumentiert. Wir testen keine Plugins.
Verbinden
Befehle und Konfigurationen nur aus den Angaben der Marktplätze. Wir führen sie nicht für Sie aus und testen keine Plugins.
Als lokaler MCP-Server: Docker-Image
Erstellt aus den Angaben von Official MCP Registry.
claude mcp add --env "ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY>" --env "GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>" --transport stdio llm-eval-harness -- docker run -i --rm -e ANTHROPIC_API_KEY -e GEMINI_API_KEY ghcr.io/razilzul/llm-eval-harness:1.0.0[mcp_servers.llm-eval-harness]
command = "docker"
args = ["run", "-i", "--rm", "-e", "ANTHROPIC_API_KEY", "-e", "GEMINI_API_KEY", "ghcr.io/razilzul/llm-eval-harness:1.0.0"]
[mcp_servers.llm-eval-harness.env]
ANTHROPIC_API_KEY = "<YOUR_ANTHROPIC_API_KEY>"
GEMINI_API_KEY = "<YOUR_GEMINI_API_KEY>"{
"mcpServers": {
"llm-eval-harness": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"ANTHROPIC_API_KEY",
"-e",
"GEMINI_API_KEY",
"ghcr.io/razilzul/llm-eval-harness:1.0.0"
],
"env": {
"ANTHROPIC_API_KEY": "<YOUR_ANTHROPIC_API_KEY>",
"GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
}
}
}
}docker run -i --rm -e "ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY>" -e "GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>" ghcr.io/razilzul/llm-eval-harness:1.0.0Ersetzen Sie die Werte in spitzen Klammern durch Ihre eigenen. Wir zeigen die Namen der Einstellungen, nie Werte.
Wo es veröffentlicht ist
| Marktplatz | Status | Version | Zahlen | Verifiziert | Erstmals gesehen |
|---|---|---|---|---|---|
| Official MCP Registry (Eintrag in der Registry öffnen) | Gelistet | 1.0.0 | Forks (GitHub): 0 Sterne (GitHub): 0 |
Nicht markiert |
Jede Zahl stammt vom jeweiligen Marktplatz und wird nie über Marktplätze hinweg addiert. Ein Marktplatz ohne veröffentlichte Zahl zeigt keine: keine Zahl ist nicht null.
Was sich geändert hat
- Wird seitdem als Produkt geführt
- Official MCP Registry: erschienen
Ähnliche Plugins
Produkte, deren eigene Namen, Beschreibungen, Werkzeuge und Skills diesem der Bedeutung nach am nächsten sind, auf allen Marktplätzen, die wir lesen. Die Nähe ist nur innerhalb dieser Liste geordnet.
-
Read, measure and explain LLM eval runs of a digline suite. Cannot promote a baseline.
-
Benchmark your own prompt on 100+ LLMs with real API calls; pick the cheapest model that passes.
-
llmtest-mcp io.github.tjacquesson/llmtest-mcp
Benchmark AI models on real prompts. Find cheaper, faster alternatives across 340+ models.
-
Compare text LLMs across OpenRouter, Bedrock, Vertex AI and Foundry with automated grading.
-
Decide, design, gate, judge, and defend evaluations for LLM, agent, and retrieval systems. Not ordinary unit tests.
-
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom…
-
metrillm io.github.MetriLLM/metrillm
Benchmark local LLM models — speed, quality & hardware fitness verdict from any MCP client
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
llm-observability-orchestration-agent-langchain-community ai.getvda/llm-observability-orchestration-agent-langchain-community
LLM Observability & Orchestration Agent (LangChain Community)
-
LLM serving benchmarks, SGLang model Day-0 support, profiling, code review, prod incident triage, PR-history dossiers, and SGLang explainer videos.
Ähnliche Plugins nach Marktplatz
Dieselbe Frage an jeden Marktplatz einzeln: Gibt es dort ein Gegenstück zu diesem Plugin?
Gelistet auf Claude Code: claude-plugins-community (5)
-
AI agent evaluation sandbox for Claude Code. Holdout scenario testing with Doer/Judge/Adversary/Observer roles, probabilistic satisfaction scoring, and append-only JSONL audit…
-
Synthetic monitoring for LLM inference endpoints. Probe TTFT, latency, throughput, and errors across OpenAI, Anthropic, Google, Azure, Bedrock, and any OpenAI-compatible server.
-
Multi-LLM orchestration MCP server. Delegate tasks to Claude Code, Codex, and Gemini CLIs with session management, retry logic, async job orchestration, and token optimization.
-
Claude model selection advisor — recommends optimal model for each task via shell hook. Zero attack surface.
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
Gelistet auf Claude Code: claude-plugins-official (5)
-
Skills for adding DeepEval evaluations, tracing, datasets, Confident AI reports, and iterative improvement loops to AI applications.
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
OpenTelemetry observability for Claude Code sessions. Captures tool calls, LLM invocations, token usage, and errors as OTel traces. Send telemetry to Dash0 or any…
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
Gelistet auf Claude: knowledge-work-plugins (5)
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
AI-first project auditor and re-engineer based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
Agent skills for Qdrant vector search covering scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across…
Gelistet auf Codex: openai-curated (openai/plugins) (5)
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
-
Build with OpenAI APIs, Agents SDK, and ChatGPT Apps, and create and save OpenAI API keys from Codex.
-
Available for US1 customers only. Analyze, investigate, and act on your Datadog telemetry directly from ChatGPT using natural language. Ask questions about your production…
-
CrowdStrike Falcon Foundry development skills for building cybersecurity applications on the Falcon platform. Includes UI development, collections, functions, workflows, API…
Gelistet auf Community GitHub marketplaces (5)
-
Decide, design, gate, judge, and defend evaluations for LLM, agent, and retrieval systems. Not ordinary unit tests.
-
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom…
-
LLM serving benchmarks, SGLang model Day-0 support, profiling, code review, prod incident triage, PR-history dossiers, and SGLang explainer videos.
-
Skills for adding DeepEval evaluations, tracing, datasets, Confident AI reports, and iterative improvement loops to AI applications.
-
Scaffolding, running, documenting, and publishing AI evaluations. Ships skills and commands for setting up eval workspaces, creating custom evals (or adapting existing…
Gelistet auf Copilot CLI: github/awesome-copilot (5)
-
Run a lightweight, role-separated AI development team with flexible tools, developer-selected models, proportionate planning, and optional QA.
-
See your local Copilot token usage by day, model, and session, and get tips to use it more efficiently.
-
Three-layer verification pipeline for AI output. Extracts claims, finds sources, and flags hallucination risks so humans can verify before acting.
-
Arize AX platform skills for LLM observability, evaluation, and optimization. Includes trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations…
-
Visualizes live session AI-credit usage as a Pac-Man board with pellets, ghosts, fruit milestones, and game-over limits.
Gelistet auf Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
Build and manage Microsoft Dataverse solutions using natural language. Includes table/column creation, solution lifecycle, data operations, and MCP server configuration.
-
model-apps
Build and deploy Power Apps generative pages for model-driven apps with specialist agents for planning, entity creation, and parallel code generation. Requires PAC CLI >= 2.7.0…
-
workiq
Query Microsoft 365 data with natural language — emails, meetings, documents, Teams messages, and more.
-
workiq-productivity
WorkIQ productivity skills powered by the local WorkIQ CLI — read-only insights across email, meetings, calendar, Teams, SharePoint, projects, and people.
Gelistet auf Cursor: cursor/plugins (5)
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
Inspect and manage feature gates, experiments, dynamic configs, and metrics.
-
Incremental transcript-driven memory updates for AGENTS.md using high-signal bullet points only.
-
Build apps, scripts, and automations with the TypeScript SDK.
Gelistet auf Docker MCP Catalog (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.
-
Wayfound’s MCP server allows business users to govern, supervise, and improve AI Agents.
-
Analyze documents, images, videos, and audio.
-
The Official Model Context Protocol (MCP) server for Kagi Search & other tools.
Gelistet auf Official MCP Registry (5)
-
Read, measure and explain LLM eval runs of a digline suite. Cannot promote a baseline.
-
Benchmark your own prompt on 100+ LLMs with real API calls; pick the cheapest model that passes.
-
llmtest-mcp io.github.tjacquesson/llmtest-mcp
Benchmark AI models on real prompts. Find cheaper, faster alternatives across 340+ models.
-
Compare text LLMs across OpenRouter, Bedrock, Vertex AI and Foundry with automated grading.
-
metrillm io.github.MetriLLM/metrillm
Benchmark local LLM models — speed, quality & hardware fitness verdict from any MCP client
Gelistet auf npm registry (MCP servers) (5)
-
One workspace for every AI coding assistant. Shared tasks, memory, and governance.
-
Read-only MCP server exposing the dsh-plugin-certification registry for DeepSeek Harness plugins.
-
easytable-mcp io.github.chrischall/easytable-mcp
easyTable restaurant reservations for Claude — availability, and create/modify/cancel bookings
-
Human-in-the-loop for AI agents over MCP elicitation: confirm/form dialogs plus a capability doctor
-
Browser-local and CLI static evidence for deployed AI model artifacts.
Nach Bedeutung gefunden, nicht über gemeinsamen Code oder ein gemeinsames Repository: Ähnliche Plugins bleiben getrennte Produkte, und dies ist weder eine Bewertung noch eine Empfehlung.
Möchten Sie dieses Zeichen für Ihr Plugin in unserem Katalog?
Belegen Sie mit einer Datei im Repository, einer Datei auf Ihrer Domain oder einem DNS-Eintrag, dass Sie es pflegen, und Sie können dieses Zeichen auf der Karte zeigen, dazu Live-Banner für Ihre Website und Ihr README. So funktioniert die Verifizierung
Außerdem erhalten Sie Hinweise, wenn ein Marktplatz es aufnimmt, entfernt, sein Verified-Zeichen oder seine Anforderungen ändert, und sein Remote-Endpunkt wird wie ein Webdienst überwacht.
Daten von Official MCP Registry. Die Zahlen stammen aus unseren eigenen regelmäßigen Durchläufen über die Marktplätze, zuletzt abgerufen am . Kontaktdaten von Plugin-Herausgebern veröffentlichen wir nicht.