Agent Radar MCP server

Agent Radar

Choose AI tools with evidence, not hype.

Agent Radar is a structured rating and recommendation knowledge base for AI Agents, Skills, MCP Servers, CLIs, Frameworks, and Prompts/Rules. It helps developers and coding agents answer a practical question: which tool fits this task, and what should I know before trusting it?

Instead of publishing another directory of links, Agent Radar turns fragmented ecosystem information into Tool Cards, explainable ratings, task-oriented recommendations, risk signals, and agent-readable data.

Why Agent Radar

The AI tooling ecosystem is growing faster than any one developer can evaluate it. Names and categories overlap, documentation quality varies, and popularity often says little about task fit, integration cost, maintenance, permissions, or trust.

Agent Radar adds a decision layer:

• Task fit over popularity. Recommendations start from the work to be done, not a global leaderboard.
• Evidence over marketing. Sources, update times, provenance, and confidence remain visible.
• Explanation over opaque ranking. Ratings and recommendations expose reasons, trade-offs, and unsuitable conditions.
• Safety before execution. Permission, trust, data-flow, and production-impact risks can tighten or block a recommendation.
• Built for humans and agents. The same reviewed knowledge is available through a Web UI, static data, HTTP endpoints, and MCP.

What You Get

Structured Tool Knowledge

Tool Cards describe capabilities, appropriate tasks, limitations, integration methods, maintenance, permissions, known risks, sources, and confidence in a consistent format across tool types.

Explainable Ratings

Ratings combine task fit, maintenance, documentation, integration cost, security risk, and evidence quality. Scores never replace the underlying evidence or risk context.

Task-Oriented Recommendations

Describe a development task, environment, preferred tool type, allowed permissions, and risk tolerance. Agent Radar returns candidates with fit reasons, risks, alternatives, and a conservative next action.

Agent-Readable Decision Context

JSON, JSONL, HTTP, and MCP surfaces let coding agents search the catalog, inspect Tool Cards, request recommendations, and explain ratings without scraping a human-only page.

How It Works

reviewed public sources
  -> normalized Tool Cards and provenance
  -> explainable ratings and risk signals
  -> task-oriented retrieval and recommendation
  -> deterministic safety enforcement
  -> provider-backed evaluation and release gates
  -> Web, JSON/JSONL, HTTP API, and MCP

Collection is controlled by a Source Registry. Candidates pass normalization, validation, review, admission, and promotion gates before entering reliable recommendation data. Dynamic recommendations use an LLM for task interpretation and candidate selection, while local deterministic logic validates known tools and enforces safety boundaries.

The v0.7 data path also discovers the current top two public repositories for GitHub topic agent-skills and expands eligible skills/**/SKILL.md manifests into one Tool Card per Skill. Repository rank is discovery evidence, not quality evidence. Each current build keeps only successfully fetched Skill manifests; raw Skill text stays inside ingestion evidence and is never sent to the recommendation provider.

Who It Is For

Shortened. The full README is on GitHub.