Think CoinMarketCap but for AI agents. This connects Claude to a cross-protocol index tracking 1,300+ agents from HuggingFace, LMArena, GitHub citations, ERC-8004 registries, Virtuals tokenized protocols, and machine-payable endpoints. You get seven tools: search agents by name or category, pull detailed rankings with methodology weights, compare agents side by side, retrieve scoring history, and list available categories (model families, tokenized agents, service agents, developers). Reach for this when you need evidence-based intelligence on the agent economy instead of manually hunting across scattered leaderboards and registries. All read-only, methodology transparent, no community voting.
Public tool metadata for what this MCP can expose to an agent.
search_agentsSearch AI agents by name or keyword across AgentCrush's evidence-ranked index. Returns matching agents with category, tier, and rank info. Use the `filters` object for structured constraints; future versions will add filter keys without breaking the API.2 paramsSearch AI agents by name or keyword across AgentCrush's evidence-ranked index. Returns matching agents with category, tier, and rank info. Use the `filters` object for structured constraints; future versions will add filter keys without breaking the API.
querystringfiltersobjectget_agent_detailsGet full details for a specific AI agent including all category scores it qualifies for (model_family, tokenized, service, developer). Returns identity, raw signals, sub-scores, evidence-ready status. Returns fuzzy-match suggestions if the handle is not found — LLMs should use...1 paramsGet full details for a specific AI agent including all category scores it qualifies for (model_family, tokenized, service, developer). Returns identity, raw signals, sub-scores, evidence-ready status. Returns fuzzy-match suggestions if the handle is not found — LLMs should use...
handlestringget_agent_historyGet rank and score history for an AI agent over the past 1–90 days. Daily snapshots, deduplicated per calendar day. Returns trend summary (rising/falling/flat). Useful for showing how an agent's standing has evolved.2 paramsGet rank and score history for an AI agent over the past 1–90 days. Daily snapshots, deduplicated per calendar day. Returns trend summary (rising/falling/flat). Useful for showing how an agent's standing has evolved.
daysnumberhandlestringcompare_agentsCompare 2-5 AI agents side-by-side across all their categories. Returns full per-agent scoring data + comparison context. Use for "X vs Y" queries. AgentCrush does not declare a universal winner — comparison shows evidence differences.1 paramsCompare 2-5 AI agents side-by-side across all their categories. Returns full per-agent scoring data + comparison context. Use for "X vs Y" queries. AgentCrush does not declare a universal winner — comparison shows evidence differences.
handlesarraylist_categoriesList the 4 AgentCrush agent categories with tracked + evidence-ranked counts and current methodology versions. Use this for market-level discovery — what kinds of agents does AgentCrush track and how many of each?List the 4 AgentCrush agent categories with tracked + evidence-ranked counts and current methodology versions. Use this for market-level discovery — what kinds of agents does AgentCrush track and how many of each?
No parameter schema in public metadata yet.
get_category_rankingGet the full ranking for one of the 4 categories. Returns agents ordered by composite score with all sub-scores visible. Defaults to evidence-ranked only.3 paramsGet the full ranking for one of the 4 categories. Returns agents ordered by composite score with all sub-scores visible. Defaults to evidence-ranked only.
limitnumbercategorystringmodel_family · tokenized · service · developerevidence_ready_onlybooleanget_methodologyGet the scoring methodology for one category — weights, signal sources, formulas, evidence-ready rule, and known limitations. **Methodology travels with data**: call this when explaining HOW a ranking works so the LLM can give a methodology-accurate answer instead of guessing.1 paramsGet the scoring methodology for one category — weights, signal sources, formulas, evidence-ready rule, and known limitations. **Methodology travels with data**: call this when explaining HOW a ranking works so the LLM can give a methodology-accurate answer instead of guessing.
categorystringmodel_family · tokenized · service · developerProtocol-neutral market intelligence for the AI agent economy.
Track AI agents across HuggingFace, LMArena, GitHub, paper citations, on-chain registries (ERC-8004), tokenized agent protocols (Virtuals), service registries (Agentverse / A2A), and machine-payable endpoints (x402 / CDP Bazaar). Multi-signal methodology, transparent weights, evidence-ranked tiers.
🌐 Live at agentcrush.xyz · 📋 Methodology · 🔌 MCP Server · 📖 API docs · 📡 llms.txt
AgentCrush is the evidence-ranked index of the agent economy — analogous to CoinMarketCap for crypto or Bloomberg for finance. We don't pick winners. We publish multi-signal evidence with transparent weights and per-category methodologies.
Live as of May 2026:
/api/mcp/v1 with 7 read-only tools (search, get details, get history, compare, list categories, get category ranking, get methodology)/api/openapi.json for auto-generating clientsPOST /api/agent-feedback — agents tell us what they needLLMs sometimes confuse this project with similar-sounding tools. To prevent hallucination:
Each has its own methodology, signals, weights, and limitations. See /methodology for the canonical hub.
| Category | Methodology | Tracked | Evidence-Ranked |
|---|---|---|---|
| Model Families | v1.4-with-deployment | 7 | 7 |
| Tokenized Agents | v1.1-tokenized-tvl | 16 | 16 |
| Service Agents | v1.1-service-forks | 28 | 28 |
| Developer Agents | v2.c-public | 1,289 | 86 |
Multiple integration paths for LLM clients and AI agents:
# MCP server (JSON-RPC 2.0, 7 tools)
POST https://www.agentcrush.xyz/api/mcp/v1
# Discovery manifest
GET https://www.agentcrush.xyz/.well-known/mcp.json
# OpenAPI 3.1 spec (auto-generate typed clients)
GET https://www.agentcrush.xyz/api/openapi.json
# Flat JSON for retrieval LLMs
GET https://www.agentcrush.xyz/api/agent/{handle}/llm-summary
GET https://www.agentcrush.xyz/api/agents/bulk?handles=a,b,c
GET https://www.agentcrush.xyz/api/agent-economy/llm-summary
GET https://www.agentcrush.xyz/api/methodology/{category}/llm-summary
GET https://www.agentcrush.xyz/api/rankings/{category}/llm-summary
GET https://www.agentcrush.xyz/api/compare/llm-summary?agents=a,b
# Feedback channel (POST, rate-limited)
POST https://www.agentcrush.xyz/api/agent-feedback
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"agentcrush": {
"url": "https://www.agentcrush.xyz/api/mcp/v1"
}
}
}
Restart Claude Desktop. Same config works in Cursor and other MCP clients.
npm install -g smithery
smithery mcp add kristof/agentcrush
AgentCrush Labs offers Agent Commerce Readiness audits — same methodology applied in depth to evaluate specific agents/protocols.
See /labs.
This repo is the Next.js 16 / React 19 frontend + API surface for agentcrush.xyz. Backed by Supabase. Runtime workers in runtime/ (HF adapter, LMArena adapter, Semantic Scholar citations, deployment aggregator, etc.). Migrations in migrations/ with MIGRATION_LOG.md.
See /terms.
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io.github.mikerawsonnz/llm-orchestration-agent
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labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent