{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_df2ytq8wvy8m","handle":"embedding-search","url":"https://wellknown.network/agents/embedding-search","links":{"self":"https://wellknown.network/agents/embedding-search/record.json","html":"https://wellknown.network/agents/embedding-search","markdown":"https://wellknown.network/agents/embedding-search/record.md","api":"https://wellknown.network/api/v1/agents/embedding-search","status":"https://wellknown.network/api/v1/agents/embedding-search/status","claim":"https://wellknown.network/agents/embedding-search/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/embedding-search/claim.json","badge":"https://wellknown.network/agents/embedding-search/badge.svg","openapi":"https://wellknown.network/openapi.json"},"ard":{"identifier":"urn:air:api.lazy-mac.com:server:embedding-search","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"embedding-search","summary":"Cloudflare Workers MCP server: embedding-search","description":"Cloudflare Workers MCP server: embedding-search","publisher":{"name":"lazymac2x","url":null},"homepage":null,"repository":"https://github.com/lazymac2x/embedding-search-api","version":"1.0.0","license":null,"protocols":["mcp"],"tags":[],"pricing":null,"endpoints":[{"url":"https://api.lazy-mac.com/embedding-search/mcp","type":"mcp_streamable_http","auth":null,"probeable":true}],"skills":null,"tools":null,"extra":{"updatedAt":"2026-05-16T11:59:00.653921Z","publishedAt":"2026-05-16T11:59:00.653921Z","registryName":"io.github.lazymac2x/embedding-search"},"attribution":{"kind":"mcp_registry","name":"mcp_registry","repoUrl":"mcp_registry","summary":"mcp_registry","version":"mcp_registry","description":"mcp_registry","publisherName":"mcp_registry"}},"derived":{"capabilities":[{"slug":"data.vector-search","name":"Vector Search","confidence":1,"provenance":"derived"},{"slug":"infra.cloud","name":"Cloud Platforms","confidence":0.99,"provenance":"derived"}],"categories":["data","infra"]},"observed":{"status":"live","statusReason":"Responded 7h ago.","lastOkAt":"2026-09-07T22:21:52.278Z","lastProbedAt":"2026-09-07T22:21:52.278Z","statusComputedAt":"2026-09-07T22:38:20.385Z","reliability30d":{"probes":4,"successRate":1,"p50Ms":56},"latestObservations":[{"at":"2026-09-07T22:21:52.278Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":69,"error":null,"detail":{"tools":[{"name":"generate_embeddings","description":"Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim)."},{"name":"semantic_search","description":"Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores."},{"name":"compute_similarity","description":"Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness."}],"toolCount":3,"serverName":"embedding-search","capabilities":["tools"],"serverVersion":"1.1.0","protocolVersion":"2024-11-05"}},{"at":"2026-09-07T15:20:40.652Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":43,"error":null,"detail":{"tools":[{"name":"generate_embeddings","description":"Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim)."},{"name":"semantic_search","description":"Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores."},{"name":"compute_similarity","description":"Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness."}],"toolCount":3,"serverName":"embedding-search","capabilities":["tools"],"serverVersion":"1.1.0","protocolVersion":"2024-11-05"}},{"at":"2026-09-07T07:31:08.036Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":32,"error":null,"detail":{"tools":[{"name":"generate_embeddings","description":"Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim)."},{"name":"semantic_search","description":"Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores."},{"name":"compute_similarity","description":"Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness."}],"toolCount":3,"serverName":"embedding-search","capabilities":["tools"],"serverVersion":"1.1.0","protocolVersion":"2024-11-05"}},{"at":"2026-09-07T00:20:38.803Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":142,"error":null,"detail":{"tools":[{"name":"generate_embeddings","description":"Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim)."},{"name":"semantic_search","description":"Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores."},{"name":"compute_similarity","description":"Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness."}],"toolCount":3,"serverName":"embedding-search","capabilities":["tools"],"serverVersion":"1.1.0","protocolVersion":"2024-11-05"}},{"at":"2026-09-06T18:24:08.881Z","kind":"mcp_initialize","ok":true,"httpStatus":200,"latencyMs":45,"error":null,"detail":{"tools":[{"name":"generate_embeddings","description":"Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim)."},{"name":"semantic_search","description":"Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores."},{"name":"compute_similarity","description":"Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness."}],"toolCount":3,"serverName":"embedding-search","capabilities":["tools"],"serverVersion":"1.1.0","protocolVersion":"2024-11-05"}}],"tools":[{"name":"generate_embeddings","description":"Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim)."},{"name":"semantic_search","description":"Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores."},{"name":"compute_similarity","description":"Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness."}],"package":null},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"mcp_registry","key":"io.github.lazymac2x/embedding-search","url":"https://registry.modelcontextprotocol.io/v0/servers/io.github.lazymac2x%2Fembedding-search","firstSeenAt":"2026-09-06T17:21:40.794Z","fetchedAt":"2026-09-06T17:21:40.794Z","normalizedAt":"2026-09-06T17:21:40.794Z"}]},"firstSeenAt":"2026-09-06T17:21:40.794Z","updatedAt":"2026-09-07T22:38:46.010Z"}