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

Even though annotations already declare the tool read-only and idempotent, the description adds meaningful behavioral context: only user-approved global and model-specific guidance is returned, and unresolved proposals or AI votes are never automatically promoted into active guidance. This helps an agent reason unexpectedly about the server testing/refreshing workflow without guessing.

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 compact and front-loaded with the most important trigger and outcome. Every sentence adds value and no sentence below the tool's return data or governance behavior, avoiding filler and giving both usage and behavioral context in just a few lines.

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?

Given the tool's low complexity, one optional parameter, full schema coverage, and clear read-only/idempotent annotations, the description fully covers what an ML agent needs: when to call, what value is returned, when to pass the optional parameter, and the governance rules around it. No important piece 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 input schema provides 100% coverage for source_model, including its optionality and alias behavior. The description goes beyond that by explaining why to pass it — it enables model-specific guidance to be layered over global guidance — which is useful semantic value beyond just the schema.

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 precise action and resource: return the TestGraph server baseline plus approved global and model-specific guidance. It also gives the specific usage context (first use, after an MCP refresh, when shared guidance is needed), making it much more informative than the bare name 'get_induction' and clearly distinct from sibling read tools that return other kinds of data.

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 call the tool: 'when first using TestGraph, after an MCP refresh, or when you need the current shared operating guidance.' This is strong guidance, but it does not name alternatives or state connot-not conditions relative to other get_* siblings, so it falls just short of a perfect score.

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