search_demand

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

With no annotations, the description carries the full burden of behavioral disclosure. It does this well by revealing that the tool queries two external sources, returns budget/bid counts and repos, and explains the interpretation of stars_per_month. However, it does not explicitly state whether the tool is read-only or mention rate limits or auth, which are common behavioral traits worth disclosing when annotations are absent.

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 longer than typical, but it is organized with a bolded lead sentence, bulleted source breakdowns, and an Args section. It front-loads the core purpose and packs domain context that helps the agent interpret results. A few sentences could be trimmed without losing value, but the structure keeps it scannable.

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 5 parameters, 0% schema coverage, and no annotations, this description supplies the needed context to call it correctly. It explains the question format, each filter's semantics, the meaning of result signals (stars_per_month), and relationship to sibling tools. The output schema covers return structure, so nothing essential 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 0%, so the description must compensate for the sparse schema. It does so thoroughly: each of the five parameters gets an actionable explanation (e.g., 'since: YYYY-MM-DD — for GitHub this filters repo CREATION date', 'min_bids: ignore gigs below this many bidders'). This adds meaning well beyond the bare property names and types.

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 a concrete, distinctive purpose: 'Find where money and effort are ALREADY moving on a problem.' It names the two data sources (Freelancer.com gigs and GitHub) and explicitly contrasts itself with search_web by calling itself the structured-evidence layer, which clearly distinguishes it from siblings without requiring schema inspection.

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 usage guidance: 'Use this BEFORE search_web on any business question' and 'Pair with search_community for the complaint in users' own words.' It explains why (transactions vs articles) and also implies when not to use alternatives, making the decision criteria unambiguous.

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