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

Annotations already establish read-only, idempotent, non-destructive behavior, so the description adds value by explaining that only approved proposals and model-specific guidance are active, and that unresolved proposals and AI votes never become active guidance automatically. That is behavioral context beyond the schema.

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 four sentences, front-loaded with a usage trigger, and every sentence contributes either a usage condition, a return-content boundary, an approval-safety rule, or parameter guidance. Nothing is redundant or extraneous.

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 simple zero-required-parameter read-only tool with no output schema, the description covers why, when, and how to call it, what it returns, which data is excluded, and how the optional parameter affects the result. An agent has enough to call this correctly without further inference.

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?

Schema coverage is 100%, so the schema already documents the optional source_model and the maxLength. The description adds practical meaning by explaining that passing source_model enables model-specific approved guidance to be layered over global guidance, going beyond a bare parameter listing.

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 ('get') and a specific resource ('TestGraph induction and governed guidance'), and clarifies what it returns: server baseline plus user-approved global and model-specific guidance. It is distinct from sibling tools because it focuses on induction/orientation guidance rather than assertions, deliberations, or contributions.

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 gives explicit call conditions: first use of TestGraph, after an MCP refresh, or whenever current shared operating guidance is needed. It does not explicitly name alternative tools or state when not to use it, but the triggering contexts are clear enough that an agent can select this tool confidently.

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