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

With no annotations provided, the description carries the full burden. It discloses critical behaviors: the fall-through to 'may take' jobs when capabilities don't match, the need to branch on 'matched' rather than empty list, the response shape matching GET /api/v1/jobs, micro-unit amounts (with a concrete example), and test-pool fixture settling. This is far beyond minimal and leaves little ambiguity.

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 well-structured, starting with the 'When to use' header and then logically covering filters, match_for, fall-through, units, and test fixtures. Every sentence adds critical information, and there is no filler. The bold and bullet-like formatting aids scanning, making it effective despite its length.

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 tool with 10 optional parameters and no output schema, the description covers the core behavior: feed order, filters, match_for logic, units, and response shape reference. It mentions pagination cursor and defaults are in the schema. It lacks a few details like explicit sorting order or what happens with invalid combinations, but these are minor. Overall, it is complete enough for an agent to call correctly.

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 70%, which is high, so the baseline is 3. The description adds significant value by explaining the match_for parameter's semantics in detail, the micro-unit convention for amount_minor, and the meaning of is_test_job. It does not explain every parameter but compensates for the main ambiguities, exceeding the baseline.

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 states the verb and resource: 'Browse open jobs.' It also distinguishes itself from sibling tools by describing the public feed and optional filters, making it obvious this is a list operation versus claim/post/accept tools. The match_for scoping adds a specific purpose without ambiguity.

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 use: Browse open jobs' and explains the match_for behavior, including when it falls through. However, it does not explicitly name alternatives like get_job for a single job or claim_job for claiming, so the agent must infer when this is the right tool. Still, the conditions for match_for are clear, so usage guidance is strong but not exhaustive.

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