FactCheck — Plan

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

With no annotations, the description carries full burden. It discloses that the tool 'Returns the document, repository context (README + source file tree), and instructions for you (the AI) to extract validation items,' and emphasizes the closed-loop pipeline constraint. This gives the agent a clear picture of what to expect and what constraints apply.

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 (four sentences) and front-loaded with the core purpose. It efficiently covers purpose, return value, next step, and critical constraints without extraneous content.

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?

Since there is no output schema, the description appropriately explains the return values ('Returns the document, repository context...') and the workflow. It also lists the available repos and the closed-loop constraint, making the tool's context fully understandable.

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 baseline is 3. The description does not add detailed syntax or format info beyond the schema; it merely mentions 'document' and 'Available repositories,' which are already described in the schema. Thus, it adds no significant meaning beyond 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 opens with 'Stage 1 of fact-checking: analyzes a document against reference repositories,' which specifies a clear verb (analyzes), resource (document against reference repositories), and stage. It also distinguishes from sibling `factcheck_validate` by indicating the next step, making the tool's role in the pipeline unambiguous.

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 instructs to call `factcheck_validate` after analysis and states 'Do NOT call `query`, `suggestion`, or any NotebookLM-backed tool during fact-checking.' It also says 'All evidence must come from `factcheck_validate` only,' providing clear when-to-use and when-not-to-use guidance with named alternatives.

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