
PersonalKnowHow MCP server
Live public demo: query one person's learning and work history as a knowledge graph via MCP.
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If you have run it, two minutes of your experience saves the next person an afternoon.
PersonalKnowHow tools (4)
write = sends, deletes, buys or postslist_by_typeFreeReturns 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.
query_knowhowFreeSearch 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.
related_entriesFreeGiven 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).
skill_evidenceFreeGiven 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.
Public scan report
scanner v0.1.9 · 2026-09-20 · same rubric, same numbers if you re-run it
- –Code scanremote-only server, no package to scann/a
- Live reliabilityremote reachable in 1025ms20/20
- Tool poisoning4 tool descriptions checked15/15
- Auth qualityopen endpoint, read-only tools10/15
- Maintenancelast push 18 days ago15/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Install directly
claude mcp add --transport http personalknowhow https://personalknowhow-demo.kxtwrdzt6g.workers.dev/mcp
PersonalKnowHow: common questions
- Is PersonalKnowHow MCP server safe?
- Yes, by our scan: it is graded A (91/100). Read the PersonalKnowHow safety report
- How do I install PersonalKnowHow?
- It runs remotely at personalknowhow-demo.kxtwrdzt6g.workers.dev. Add it to Claude Code, Claude Desktop or Cursor with the snippets above, or call it through the mcp.market gateway without installing anything.
- Does PersonalKnowHow need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is PersonalKnowHow maintained?
- The last commit was 19 days ago (2026-09-01). The latest release is v1.0.0.
- Is PersonalKnowHow up?
- 100% of our last 5 checks got an answer. We check remote servers about four times a day.
- What can I use instead of PersonalKnowHow?
- Servers from other publishers that do the same job: Codebase Memory MCP server.
Alternatives to PersonalKnowHow
Same job from other publishers: the closest match first, then the best rated.
- Codebase MemoryCodebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.not reviewedWidely usedC