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

The description adds meaningful behavioral context beyond annotations: a human reviews every submission, accepted gifts become part of JOBFLOW's knowledge, submitters are thanked via /thanks, no personal data is allowed, and it's free with no key needed. The annotations only state basic hints (readOnlyHint=false, etc.), so this additional context is valuable. It doesn't disclose rate limits or whether submissions are editable, but covers the core interaction model well.

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 a single long, poetic paragraph that mixes purpose, usage, behavioral details, and constraints. It is front-loaded with the purpose, but the elaborate tone and run-on sentence reduce scannability. It could be more structured and concise while retaining the essential 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?

Given 5 parameters, 80% schema coverage, no output schema, and annotations that are minimal, the description covers the tool's purpose, usage, behavioral expectations, and constraints (no personal data, free, no key). It is complete enough for an agent to invoke correctly, though it could mention response timing or review outcomes more explicitly.

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 80%, so the schema already documents most parameters (from, lang, text, trade). The description explains the kinds enum in detail, which is helpful, but adds little for the other parameters beyond what the schema provides. Baseline 3 is appropriate when schema does most of the work.

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 clearly establishes the tool as a way to submit knowledge/feedback to JOBFLOW, and the six kinds are enumerated with explanation. It distinguishes itself from siblings like agents_post or intake_classify, though the poetic framing ('leave a gift') adds slight ambiguity about whether this is a formal submission mechanism.

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?

It says when to use the tool—'If you know something JOBFLOW should know'—and defines each kind with examples of when each applies. It does not name alternatives or explicitly say when not to use it, but the enumerated kinds effectively guide the agent to the right category.

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