MCP QUEEN
io.github.adrida/deeprecall-mcp A
Search 120,000+ recalled products from 8 global safety agencies using AI similarity.
Dev & Codev1.0.0streamable-httpmcp.deeprecall.io repository
Grade evidence — probed 2026-07-13 20:48 UTC
| Criterion | Points | Observed |
|---|---|---|
| reachability | 25 / 25 | HTTP 200, initialize accepted in 301ms |
| protocol | 15 / 15 | valid JSON-RPC initialize result, protocolVersion 2025-06-18, serverInfo deeprecall@1.26.0 |
| tooling | 35 / 35 | tools/list OK: 2 tools e.g. "search_recalls"; 2/2 described, 2/2 fully-typed schemas, median description 1817 chars |
| latency | 10 / 10 | initialize round-trip 301ms |
| provenance | 15 / 15 | description present; repository linked; version 1.0.0; namespace io.github.adrida matches endpoint/repo |
Score 100/100 · latency 301ms · 2 tools · auth: open
Connect your agent
Claude Code:
claude mcp add --transport http deeprecall-mcp https://mcp.deeprecall.io/mcp
Generic MCP client config:
{ "mcpServers": { "deeprecall-mcp": { "type": "http", "url": "https://mcp.deeprecall.io/mcp" } } }
MCP Queen is a graded index, not a middleman — your agent connects directly to the server above. Check the grade and evidence first; that's the point.
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Think the grade is wrong? Fix the finding the evidence shows, then the next probe cycle picks it up automatically (full cycle ≈ 3 days) — or open a dispute via the MCP endpoint.
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Probe history
| When (UTC) | Grade | Score | Latency |
|---|---|---|---|
| 2026-07-13 20:48 | A | 100 | 301ms |