Clarvia Aeo Check

Clarvia AEO Check

Overview

Before adding any MCP server, API, or CLI tool to your agent workflow, use Clarvia to score its agent-readiness. Clarvia evaluates 15,400+ AI tools across four AEO dimensions: API accessibility, data structuring, agent compatibility, and trust signals.

Prerequisites

Add Clarvia MCP server to your config:

{
  "mcpServers": {
    "clarvia": {
      "command": "npx",
      "args": ["-y", "clarvia-mcp-server"]
    }
  }
}

When to Use This Skill

• Use when evaluating a new MCP server before adding it to your config
• Use when comparing two tools for the same job
• Use when building an agent that selects tools dynamically
• Use when you want to find the highest-quality tool in a category

How It Works

Step 1: Score a specific tool

Ask Claude to score any tool by URL or name:

Score https://github.com/example/my-mcp-server for agent-readiness

Clarvia returns a 0-100 AEO score with breakdown across four dimensions.

Step 2: Search tools by category

Find the top-rated database MCP servers using Clarvia

Returns ranked results from 15,400+ indexed tools.

Step 3: Compare tools head-to-head

Compare supabase-mcp vs firebase-mcp using Clarvia

Returns side-by-side score breakdown with a recommendation.

Step 4: Check leaderboard

Show me the top 10 MCP servers for authentication using Clarvia

Examples

Example 1: Evaluate before installing

Before I add this MCP server to my config, score it:
https://github.com/example/new-tool

Use the clarvia aeo_score tool and tell me if it's agent-ready.

Example 2: Find best tool in category

I need an MCP server for web scraping. Use Clarvia to find the 
top-rated options and compare the top 3.

Example 3: CI/CD quality gate

Add to your CI pipeline using the GitHub Action:

- uses: clarvia-project/clarvia-action@v1
  with:
    url: https://your-api.com
    fail-under: 70

AEO Score Interpretation

Best Practices

• ✅ Score tools before adding them to long-running agent workflows
• ✅ Use Clarvia's leaderboard to discover alternatives you haven't considered
• ✅ Re-check scores periodically — tools improve over time
• ❌ Don't skip scoring for "well-known" tools — even popular tools can score poorly
• ❌ Don't use tools scoring below 50 in production agent pipelines without understanding the limitations

Common Pitfalls

• Problem: Clarvia returns "not found" for a tool Solution: Try scanning by URL directly with aeo_score — Clarvia will score it on-demand

• Problem: Score seems low for a tool I trust Solution: Use get_score_breakdown to see which dimensions are weak and decide if they matter for your use case

Related Skills

• @mcp-builder - Build a new MCP server that scores well on AEO
• @agent-evaluation - Broader agent quality evaluation framework

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags clarvia-aeo-check <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns