personalknowhow-demo

endpoint

https://personalknowhow-demo.kxtwrdzt6g.workers.dev/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is personalknowhow-demo live?

Yes — it answered the hub's last check (checked 17m ago). It answered 99% of checks over the last 30 days.

Is personalknowhow-demo free to use?

Yes — the hub reached it with no key and no payment.

What tools does personalknowhow-demo have?

4 tools: query_knowhow, skill_evidence, list_by_type, related_entries.

Is personalknowhow-demo safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• query_knowhow reads unknown 17m ago

  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.

  mcp-tool

  12345678910111213

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "topic"
    ],
    "properties": {
      "topic": {
        "type": "string",
        "description": "A skill, technology, or topic to check, e.g. 'django' or 'aws'"
      }
    }
  }

  arguments 13 lines copy
• skill_evidence reads unknown 9h ago

  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.

  mcp-tool

  12345678910111213

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "tag"
    ],
    "properties": {
      "tag": {
        "type": "string",
        "description": "An exact tag from a prior result's tags array, e.g. 'python', 'docker', 'gcp'"
      }
    }
  }

  arguments 13 lines copy
• list_by_type reads unknown 17m ago

  Returns the COMPLETE, exact set of entries for one type, with no similarity ranking, no relevance cutoff, and no cap on count. Use this instead of query_knowhow whenever the question requires an exhaustive or countable answer ('list all my certifications', 'how many courses have I completed'). Deterministic ordering (sorted by label) -- repeated calls with the same type return the same list in the same order.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "type"
    ],
    "properties": {
      "type": {
        "enum": [
          "course",
          "project",
          "certification",
          "education",
          "endorsement",
          "position",
          "profile",
          "recommendation",
          "article",
          "organization",
          "language",
          "honor",
          "publication",
          "patent",
          "volunteering",
          "test_score",
          "skill_assessment"
        ],
        "type": "string",
        "description": "Exact entry type to list in full"
      }
    }
  }

  arguments 32 lines copy
• related_entries reads unknown 17m ago

  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).

  mcp-tool

  12345678910111213

  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "id"
    ],
    "properties": {
      "id": {
        "type": "string",
        "description": "An entry id from a prior query_knowhow or list_by_type result"
      }
    }
  }

  arguments 13 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.