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rebuild_entity_graph

Rebuild the entity knowledge graph over your brain using local Claude extraction; results are cached for instant access.

Instructions

(Re)build the Graphify entity knowledge graph over the brain.

Runs extraction via the local claude CLI (free; uses your Claude plan). Requires graphifyy installed (pip install graphifyy). Cached under BRAIN_DIR/.constellation so other tools read it instantly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that extraction runs via the local `claude` CLI, requires `graphifyy`, and caches results under BRAIN_DIR/.constellation for fast access by other tools. It does not mention potential destructive overwriting or performance details, but the provided context is substantive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (four sentences), front-loads the primary purpose, and each sentence adds value: purpose, execution method, dependency, and caching behavior. There is no redundant or filler text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides useful context (caching, prerequisites, execution method) and an output schema exists, so return values need not be described. However, the unexplained `project` parameter and lack of guidance about when to use versus alternatives make the description incomplete for a fully informed decision.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one optional parameter (`project`) with 0% description coverage, and the description does not mention this parameter at all. The description should have explained what `project` controls (e.g., scope or target), but it remains completely undocumented, leaving a significant gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: '(Re)build the Graphify entity knowledge graph over the brain.' It uses a specific verb (rebuild) and resource (entity knowledge graph), and distinguishes it from sibling tools like `memory_graph` by naming Graphify and mentioning extraction via the local `claude` CLI.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (to build/rebuild the graph) and mentions prerequisites (graphifyy installed) and side effects (caching). However, it does not explicitly contrast with alternatives like `memory_graph` or state when not to use this tool, leaving usage guidance largely implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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