kernel_graph_schema

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

No annotations are supplied, so the description carries the full burden. It discloses the shape of the returned information, which is genuinely useful, but it says nothing about whether the call is side-effect free, how expensive or fresh the counts are, or whether the output is cached. For a pure introspection tool the risk is low, but the disclosure is only partial.

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?

Two sentences, both earning their place: the first front-loads the returned content in a compact enumeration, the second gives the ordering rule. No filler or restatement of the tool name.

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?

With an output schema present, the description does not need to explain return values, and for a zero-parameter read it covers purpose and sequencing well. The only omission is any statement about cost, freshness, or safety expectations, which would round out a no-annotation definition.

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 tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline of 4 applies; no parameter-level detail is needed or missing.

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 enumerates exactly what the tool exposes: node labels with counts and properties, label-to-label relationship mappings, and vector plus full-text search indexes. It also distinguishes itself from the sibling query tool by positioning itself as the discovery step, so an agent can tell what it returns without inspecting anything else.

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 final sentence gives explicit sequencing guidance: call this before kernel_graph_query. That is a clear usage condition. It stops short of stating when not to call it (for example, when the agent already knows the label names) or naming other alternatives beyond the query tool.

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