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Deterministic ordering (sorted by label) -- repeated calls with the same type return the same list in the same order.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"type":{"description":"Exact entry type to list in full","enum":["course","project","certification","education","endorsement","position","profile","recommendation","article","organization","language","honor","publication","patent","volunteering","test_score","skill_assessment"],"type":"string"}},"required":["type"],"type":"object"},"name":"list_by_type"},{"description":"Search this person's real, grounded skills/experience graph for a topic using semantic search. Returns only entries with real evidence -- never guesses. Every entry here represents something actually done or completed (project, certification, position, course, or education) -- this public dataset never includes saved-but-not-worked jobs or applications. This is SEMANTIC search ranked by relevance and capped at 10 results -- it is NOT exhaustive. For 'list every X' or 'how many X' questions, use list_by_type instead -- it returns the complete, uncapped set with no similarity ranking involved. Clearing the similarity floor means 'closest available match', not 'confirmed match' -- read each result's actual label/description/type before citing it as evidence for the specific topic queried. Each result also carries source_url/captured_at/provider (the real evidence behind it, when available) and source_note (explaining why not, when the underlying source has no link) -- use these to answer a disputed claim with actual backing evidence rather than just the description text. Embeddings can rank a topically-adjacent-but-wrong entry above the floor (e.g. a course on a different cloud data-warehouse tool, or a different framework in the same category) for a term it isn't actually about; if a result isn't genuinely on topic, treat the query as unmatched rather than reporting it as a match. For 'what else is connected to this' or 'what shares a skill/provider with this specific entry' questions, call related_entries with a result's id instead of re-querying by topic.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"topic":{"description":"A skill, technology, or topic to check, e.g. 'django' or 'aws'","type":"string"}},"required":["topic"],"type":"object"},"name":"query_knowhow"},{"description":"Given an entry id (from a prior query_knowhow or list_by_type result), returns other entries that share at least one tag or the same content provider -- the only two relationships this corpus currently tracks (there is no 'led to' or 'used in' relationship here, only shared tag/provider). This is NOT a similarity or relevance judgment -- two entries sharing a broad tag (e.g. both tagged 'data-science') can be quite different in substance; read each related entry's own label/type before treating it as meaningful. Each group is capped at 15 entries, sorted by label, with the true total count shown separately so you know if results were truncated -- call list_by_type on that type if you need the full set. Useful for 'what else is connected to X' or 'what did they do that relates to this specific course/certification/endorsement' -- questions query_knowhow's independent similarity search can't reliably answer, since two entries can be genuinely related without their description text reading alike (e.g. a course title and an endorsement phrase for the same skill, worded completely differently).","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"id":{"description":"An entry id from a prior query_knowhow or list_by_type result","type":"string"}},"required":["id"],"type":"object"},"name":"related_entries"},{"description":"Given an exact tag/skill (e.g. 'docker', 'gcp'), returns EVERY entry with that tag, uncapped, grouped by type with a real count per type. Unlike related_entries (capped at 15, requires a starting entry id) or query_knowhow (semantic, ranked, may over- or under-include), this is an EXACT tag match against every entry -- the right tool for 'how many X have I completed/done' or 'do I have any real evidence for X at all'. Tags are exact strings from a prior list_by_type/related_entries/query_knowhow result's tags array -- this is NOT semantic search; a tag never assigned during ingest returns found:false, try query_knowhow instead. Each type's entries sort by captured_at ascending (oldest first); entries with no captured_at are moved to the end and counted in undated_count, never silently sorted as if their date were known.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"tag":{"description":"An exact tag from a prior result's tags array, e.g. 'python', 'docker', 'gcp'","type":"string"}},"required":["tag"],"type":"object"},"name":"skill_evidence"}]},"body_sha256":"398c8cd0d397eca5e5fcedc44dd1933558d4e57e768161e53ff086483a9ed32e","prev_id":null,"leaf":"0x5284578315786e04668eec6cbe785c575f3bbfd782d07c6cde79902c00f22024","leaf_text":"{\"body\":\"398c8cd0d397eca5e5fcedc44dd1933558d4e57e768161e53ff086483a9ed32e\",\"id\":571552,\"kind\":\"mcp.tools\",\"method\":\"probe.mcp/1\",\"observed_us\":1793140296935796,\"prev\":null,\"subject\":\"8b6e711a-30a4-4438-be90-470c170c71a2\",\"subject_kind\":\"endpoint\"}","day":{"day":"2026-10-27","leaves":273461,"root":"0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795","chained":"0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4","anchor_chain":"eip155:5042002","anchor_tx":"0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4","anchor_block":69414147},"proof":["0x5284253e3d2e68bc70d8c7a01daef774629a1d47e882b820fe15441a384b7205","0xf096942c45cc90cff51915cacfd67a6bcc3ae5450c4d8b08ea97e57c9a78910f","0x0a400c303d9d42d13592bee1387b15a79101690460be96c387104e32a7644587","0x2f54dce965116ff28b77ed79b79d3777eb696864643c559e0f6dba40db5b815f","0x135cfa036bbecfbbccbbcdf568bd73ab61572952a2aeaafd17cc72a681a50875","0xfba6b071ba6fbab970d589b00461e0f137e4afec2a223e4851913aa8283a0a95","0x7b7e09ef634d10151b3e6b1107d79165fdf8e98bbe752f39a984d3d636f80c5b","0x3ebd5ef32204b926385fb99752e31b1d76171f044129a0639db6f67c3fe16711","0x84300770960b4f03f2d296a091f1c4931ecb838b0a9470137c3e523dd0225aef","0x2c7eacabc74fc573690fc9a970f9c763fd5121c991acbecc3d1007fac5a9d364","0xf08d73dbf9855d720c0a2a3ccd264381a0c543dccc463bf9362cb3ec368ba1ac","0xd57b5a86cf210d9ed4e220de5668cf59c88f82934bf7c8ef9e6b3e894577973c","0x996f3d89f9508378f3c194c9828b500725dd599c52fd040349190982338d96f2","0x9ea0c6a53f80a30b6f13671109f98a000cf98e581875129f4be4b57131d06364","0xb78ea6a255dd06e2f402a1a733bca392fa6243920bcba95e8b80bdfc6ce5f553","0x3ed50f053eca72dfd3a9d45a11935240bfe691e57835c367ddebe2cbc49efe9d","0x0e01aa9525097570a6edaf32bfc959de57bf5c24317cf10c0716affea85ca60d","0xa20d48798a34aba12eb9c7297722982d910f06c148f5e27ac59307422964a585","0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699"]}