record-fact

Wrapper around mcp__neurodock-cognitive-graph__record_fact that shows the exact valid input shape up front. The bare tool has strict validation; load this skill first and call it correctly on the first attempt.

Authoritative schema $id: https://schemas.neurodock.org/mcp-cognitive-graph/v0.1.0/record_fact.schema.json (source).

When to use

• The user says "remember X", "make a note that X", "log that X".
• A skill needs to persist an attribution, a decision, a dependency, or a

tag into the graph.

• After a meeting brief identifies a commitment worth retaining.

The shape you MUST send

A fact is a triple: subject, predicate, object. Flat shapes like {fact: "..."} are rejected with SUBJECT_REQUIRED. Subject and object are entity dictionaries, not strings.

Entity dictionary

{ "type": "<entity-type>", "name": "<display name>" }

Optional id field if you already know the canonical id.

v0.1.0 entity types (closed enum)

• person
• project
• decision
• concept
• source

Common wrong values that get rejected: feature, bug, task, note, user, goal. Map them: a feature is a concept; a person's name is person; an architectural choice is decision.

v0.1.0 predicates (closed enum)

• mentioned_in
• decided_in
• reports_to
• depends_on
• resolved_by
• blocked_by
• tagged
• belongs_to

Common wrong values: has_bug, is_a, causes. Free-form predicates land in v0.2 via type_extensions. For v0.1.0, pick the closest verb above — most situations map to blocked_by, depends_on, mentioned_in, or tagged.

Object can be a literal

When the object is a tag, status, or short note rather than an entity:

{ "literal": "external-memory" }

How to invoke — canonical examples

Example 1: a decision attribution

{
  "subject": { "type": "person", "name": "Roberto" },
  "predicate": "decided_in",
  "object": { "type": "decision", "name": "Adopt SQLite + sqlite-vec" },
  "source": "msg://slack/C123/p1715683200000100",
  "confidence": 1.0
}

Example 2: a dependency between concepts

{
  "subject": { "type": "project", "name": "NeuroDock" },
  "predicate": "depends_on",
  "object": { "type": "concept", "name": "sqlite-vec" }
}

Example 3: a tag (object is a literal)

{
  "subject": { "type": "project", "name": "NeuroDock" },
  "predicate": "tagged",
  "object": { "literal": "external-memory" },
  "confidence": 0.8
}

Example 4: a bug-blocks-a-feature relationship

User says "remember that the Gmail translation bug is blocking the LM Studio launch". Both sides model as concept because v0.1.0 has no feature or bug type:

{
  "subject": { "type": "concept", "name": "LM Studio launch" },
  "predicate": "blocked_by",
  "object": { "type": "concept", "name": "Gmail translation silent failure" }
}

Errors and how to recover

Side effects

• Auto-creates referenced entities by (type, name) if they do not exist.
• Returns auto_created_entities[] so callers can show what was created.
• Idempotent at (subject, predicate, object) — duplicate calls return the

same fact_id with deduplicated: true.

Limitations

• v0.1.0 vocabulary is small. If nothing fits, model it as tagged with a

literal object and revisit when v0.2 extension predicates land.

• Auto-create can fragment the graph if you keep creating concept

entities from long free-text descriptions. Prefer short canonical names ("Gmail translation bug") over sentences.

• The tool is local-only; no remote sync in v0.1.0.

Voice

If you need to translate the user's plain English into a triple, show the mapping briefly ("logging this as concept blocked_by concept") so the user can correct you in one turn. Do not dump the full schema at them. Quote any auto_created_entities back so they can spot duplicates.