Infrastructure

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Qdrant is an Open-Source Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.

Scanned 4h ago · 9 views · Open site

Checks

• HTTPS5/5

  Served over HTTPS.

• llms.txt12/12

  Found /llms.txt (157632 chars).

• Agent manifest0/15

  No /.well-known/ai-agent.json.

  Fix: Publish https://qdrant.tech/.well-known/ai-agent.json with name, description, capabilities, contact, auth, and pricing.

• A2A agent card0/12

  No /.well-known/agent-card.json.

  Fix: Publish https://qdrant.tech/.well-known/agent-card.json with name, url, and skills or capabilities.

• MCP endpoint0/12

  No MCP document or endpoint found.

  Fix: Publish /.well-known/mcp.json (or an MCP resource metadata document) pointing at your MCP server.

• OpenAPI spec0/12

  No OpenAPI or Swagger spec found.

  Fix: Serve /openapi.json (or /swagger.json) with a valid OpenAPI spec, or link it from the homepage.

• Named capabilities0/8

  No named capabilities, skills, or API operations.

  Fix: List named capabilities in the agent manifest, agent card skills, or OpenAPI path summaries.

• I/O schemas0/8

  No I/O schemas.

  Fix: Add components.schemas / requestBody schemas in OpenAPI, or input/output schemas on each skill.

• Machine-readable pricing4/8

  Pricing mentioned in llms.txt, but not as structured data.

  Fix: Add a pricing object to ai-agent.json, or publish /pricing.json with currency and product amounts.

• Auth, contact, rate limits5/8

  Declared: contact, auth.

  Fix: Declare contact, auth, and rateLimit in ai-agent.json (and OpenAPI info.contact / securitySchemes).

8 checks short of a perfect 100. The fix lines above are the report — what to publish, at which path.

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