Flowwoods Research Recruiting

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safe-read profile is covered. The description goes further and usefully discloses that this creates no charge, reservation, guaranteed availability, or binding quote — exactly the kind of caveat an agent needs before presenting the number to a user.

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?

Two sentences, no filler, and the core purpose plus the strongest caveat lead. The disclaimer and the incentive-omission rule each earn their place and are placed after the purpose rather than burying it.

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?

An output schema exists, so return values need not be explained, and the annotations carry the safety profile. The non-binding framing plus incentive semantics make this nearly complete; only session_count's role is left entirely to the schema.

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 carry parameter meaning, and it only covers one of two: it explains that incentive_per_person_usd is a caller-specified budget and that omitting it yields an unknown total. session_count is never mentioned, though the schema's min/max/default of 3 constrains it adequately on its own.

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 specific verb and resource — estimating a recruiting fee plus an incentive budget — which is a concrete, non-tautological purpose. It does not, however, name or distinguish itself from the siblings get_service_offer or submit_research_brief, so an agent must infer the boundary rather than read it.

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?

"Omit incentives to show that the total is unknown" gives real guidance for one input, but there is no statement of when to call this tool versus get_service_offer or submit_research_brief. Usage is implied by the non-binding framing rather than spelled out.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false; 'No side effects' largely restates the read-only hint. The description does add real provenance context ('Public provider facts, not an endorsement') and warns that availability is unconfirmed, which is useful framing the annotations do not convey.

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?

Three short, front-loaded sentences with no filler. The closing 'No side effects' sentence is redundant against readOnlyHint, slightly diluting an otherwise tight description.

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?

A zero-parameter read tool with an output schema, so return values need no explanation. The data scope and the trust caveat are covered; only the relationship to sibling tools is missing, which is a usage gap rather than a completeness one.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. Schema coverage is 100% regardless.

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?

States a specific verb ('Read') and enumerates the resource contents: recruiting scope, pilot pricing, exclusions, availability. An agent can tell what it returns, though it never explicitly differentiates itself from estimate_research_cost or submit_research_brief.

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?

No when-to-use guidance, no prerequisites, and no mention of the sibling tools. The implied flow (read the offer before estimating cost or submitting a brief) is left entirely to inference.

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

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

Beyond the annotations (readOnlyHint=false, idempotentHint=true, openWorldHint=true), the description discloses what is shared and with whom, that user authorization and privacy/contact consent are required, and that no order/reservation/charge occurs. It also explains how to retry idempotently via request_id reuse, adding real operational context.

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?

Five short, front-loaded sentences with no filler. Each sentence adds distinct information: what it sends, what it shares, requirements, negative guarantees, and retry semantics.

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 one-parameter submission tool with an output schema (so return values need no explanation) and rich annotations, the description covers cost, side effects, consent prerequisites, and idempotency. An agent has everything needed to invoke it 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?

Although reported schema description coverage is 0%, the description conveys the nature of the payload: contact details plus research details, plus authorization and consent flags, plus request_id reuse semantics. This compensates well for the nested Brief object, though it doesn't enumerate individual fields like preferred_timing or product_url.

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 names a specific verb and resource: sending a free feasibility inquiry (research brief) to an operator for follow-up. It does not explicitly contrast with its siblings (estimate_research_cost, get_service_offer), so an agent must infer the routing, but the action itself is 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?

It clarifies that this is a FREE feasibility inquiry and explicitly rules out order creation, participant reservation, and charging, which helps an agent understand the tool's scope. However, it never states when to prefer this over the sibling tools (e.g., calling estimate_research_cost first), leaving usage implied rather than directed.

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