brain_graph

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, and it delivers. It discloses both invocation modes, what each returns, that relations cite their source files, and that the graph is built from LLM-extracted triples in graph.json via `loci graph build`. This gives the agent a clear picture of behavior and data provenance.

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

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

The description is concise and front-loaded: the main action is in the first sentence, followed by behavior, provenance, and usage. Every sentence contributes a distinct piece of information, and there is no filler or redundancy.

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

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

For a single-optional-parameter read tool with no output schema and no annotations, the description is complete. It explains both output modes, the return characteristics (relations with source citations, hub entities), data provenance, and the intended usage context. Nothing needed to invoke it correctly is missing.

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

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

The schema already documents the single optional parameter at 100% coverage, so the baseline is 3. The description adds meaningful semantics by tying the presence or absence of the entity parameter to different outputs: relations for a given entity versus top hub entities when omitted.

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

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

The description states a specific verb and resource: it queries the knowledge graph over memories and wiki pages. It further specifies two concrete behaviors — returning outgoing/incoming relations for an entity or top hub entities when omitted — which makes its scope and output mode clear and distinct from siblings like brain_search and brain_ask.

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

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

The description explicitly says when to use it: explore how concepts connect before asking questions and see the strongest entities for orientation. It does not name alternative tools or state when not to use it, so it stops short of full routing guidance.

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