PersonalKnowHow

PersonalKnowHow MCP — public demo

Unauthenticated MCP server over the public export of one person's knowledge graph (public_entries.json + public_embeddings.json, built by ingest/build_public_export.py's fail-closed allowlist). Every entry here represents something actually done or completed (course, project, certification, position, education, etc.) — career-interest/job-application "signal" evidence is structurally excluded from this export, never just filtered at query time.

Tools

query_knowhow(topic: string)

Semantic search, ranked by relevance, capped at 10 results. Not exhaustive — a real match can rank below the cutoff.

query_knowhow({ topic: "django" })

→ { "found": true, "count": 4, "evidence": [ { "id": "github_django_docker_db", "label": "django_docker_db", "type": "project", "score": 0.735, ... }, { "id": "github_django_docker", "label": "django_docker", "type": "project", "score": 0.71, ... }, { "id": "github_startup", "label": "StartUp", "type": "project", "score": 0.706, ... }, { "id": "github_news_sentiment_app", "label": "News-sentiment-app", "type": "project", "score": 0.676, ... } ] }

list_by_type(type: string)

The complete, exact set of entries for one type — no ranking, no cap.

list_by_type({ type: "certification" })

→ { "type": "certification", "count": N, "entries": [ { "id": "...", "label": "...", ... }, ... ] }

related_entries(id: string)

Given an entry id, returns other entries sharing at least one tag or the same provider — capped at 15 per group, true count always shown.

related_entries({ id: "github_django_docker" })

→ { "id": "github_django_docker", "type": "project", "related_by_tag": { "docker": { "count": 4, "entries": [ { "id": "github_django_docker_db", "label": "django_docker_db", "type": "project" }, ... ] }, "aws": { "count": 16, "entries": [ ... ] }, "cloud": { "count": 51, "entries": [ ... ] }, "devops": { "count": 10, "entries": [ ... ] } }, "related_by_provider": { "count": 36, "entries": [ ... ] } }

(Note: this example shows `related_entries` only clustering `django_docker` with the one other

Django repo sharing an unrelated tag — neither StartUp nor News-sentiment-app appear, since neither shares a tag with it. skill_evidence({ tag: "docker" }) below finds all of them instead, since it matches on the tag directly rather than requiring a shared-tag path from one specific starting entry.)

skill_evidence(tag: string)

Exact tag match (not semantic), uncapped, grouped by type with real counts. The right tool for "how many X have I actually done" or "do I have any real evidence for X at all."

skill_evidence({ tag: "docker" })

→ { "tag": "docker", "found": true, "count": 5, "by_type": { "project": { "count": 4, "undated_count": 0, "entries": [ { "id": "github_django_docker", "label": "django_docker", "captured_at": "2021-06-10" }, { "id": "github_django_docker_db", "label": "django_docker_db", "captured_at": "2021-06-22" }, { "id": "github_bedex", "label": "BedEx", "captured_at": "2025-05-29" }, { "id": "github_terraform_associate_prep", "label": "terraform-associate-prep", "captured_at": "2026-08-03" } ] }, "endorsement": { "count": 1, "undated_count": 0, "entries": [ { "id": "linkedin_endorsements_docker_products_202209", "label": "Endorsed: Docker Products", "captured_at": "2022-09-21" } ] } } }

skill_evidence({ tag: "gcp" }) → { "tag": "gcp", "found": true, "count": 2, "by_type": { "position": { "count": 1, "undated_count": 0, "entries": [ { "id": "linkedin_positions_data_engineer_data_analyst_at_biites_202107", "label": "Data Engineer & Data Analyst at Biites", "captured_at": "2021-07-01" } ] }, "project": { "count": 1, "undated_count": 0, "entries": [ { "id": "projects_connector", "label": "connector", "captured_at": "2026-08-09" } ] } } }

(The private tier's `skill_evidence({ tag: "gcp" })` also surfaces a `signal_only` career-interest

entry — that entire category is excluded from this public export, so this tier's count is lower by design, not by omission.)

An unrecognized tag returns found: false — exact match against tags assigned during ingest, not a fuzzy/semantic lookup. If a skill genuinely exists but was never tagged, query_knowhow may still find it (it searches full entry text, not just tags).