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

Annotations already declare readOnlyHint=true, and the description adds valuable behavioral context: nothing is persisted or executed, the pull runs server-side with a stored GitHub token, private repos are supported, and the AI client never needs repo access. This goes beyond the annotation and clarifies side effects, auth, and privacy.

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 dense but focused: three sentences front-load the core purpose, then add workflow, cost rationale, and privacy/read-only facts. Slightly long due to embedded pipeline details, but every clause contributes useful information.

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. It covers the input constraints, expected workflow, read-only behavior, private repo handling, and server-side token use. Nothing essential for correct invocation 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?

Schema description coverage is 100%, so the baseline is 3, but the description adds meaningful access semantics: the repo must be readable by the connected GitHub token, private repos work because the pull is server-side, and refs default appropriately. This helps the agent choose parameter values correctly.

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 verb and resource: pull the caller's own GitHub repo via their connected token and return deterministic grounding plus a field contract. It enumerates the specific outputs (basics, languages, tech domains, detected tools and hosts) and distinguishes the tool from its siblings by naming the downstream workflow (findagent_submission_wizard, findagent_create_draft).

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 workflow guidance: use the grounding as the basis, then run findagent_submission_wizard and finalize with findagent_create_draft. It also explains that the agent's own model does synthesis, avoiding FindAgent LLM cost. It does not spell out when-not-to-use conditions or alternatives, but the placement in the pipeline is clear.

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