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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does usefully disclose that a missing JEV_API_KEY triggers setup guidance, but it does not state whether the tool sends emails or only returns drafts, nor any other failure modes or side effects.

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 with no filler. The primary action is front-loaded, and the missing-key behavior is conveyed in a single additional sentence. Every word earns its place.

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 simple 3-parameter drafting tool, the description covers the core output and a key error case. However, since there is no output schema, it could be more explicit about what a successful response contains, though 'Draft... email' strongly implies the returned drafts.

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 100%, so the schema already documents all three parameters. The description adds no extra meaning about kolHandle, offerDetails, or campaignDescription beyond what the schema provides, so the baseline score of 3 applies.

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 action ('Draft') and the specific deliverable ('a first collaboration email and a follow-up for day 3'). This is distinct from the sibling tools search_kols and score_fit, which perform search and scoring rather than content generation.

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?

There is no guidance about when to choose this tool over search_kols or score_fit, nor any mention of prerequisites or placement in a workflow. The only conditional information is the missing-key behavior, which is not a usage guideline.

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?

With no annotations, the description carries the behavioral disclosure burden. It usefully notes that a missing JEV_API_KEY returns setup guidance, but it does not disclose the success output format, whether the operation is read-only, or other failure modes. Some context is added, but significant behavioral gaps remain.

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 with no wasted words. The main purpose is front-loaded, and the important API-key edge case is included as a compact second sentence.

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 simple three-parameter scoring tool, this is fairly complete: it states what the tool judges, implies the output concept, and flags a key environment dependency. It does not detail the return payload, but no output schema exists and the description still gives enough context for correct invocation.

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 100%, so the schema already documents all three parameters. The description adds context about evaluation dimensions (niche, tone, price band) but does not map them to specific parameters or add meaning beyond the parameter descriptions. Baseline 3 is appropriate.

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 tool's action: ask Jev System One to judge niche, tone, price band, and outreach-worthiness. This is specific and distinct from sibling tools like search_kols and draft_email, so there is no ambiguity about what the tool does.

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 implies this tool is used when a fit judgment is needed, but it does not explicitly say when to use it versus alternatives like search_kols or draft_email, nor does it explain whether it should precede or follow those tools. Usage context is only implied, not stated.

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?

With no annotations provided, the description carries the full burden and excels. It discloses the read order (local SQLite, then shared pool if enabled and thin, otherwise Apify), the Jev labeling of fresh profiles, upload behavior conditioned on sharing, and the graceful handling of a missing APIFY_API_TOKEN (returns setup guidance without exiting). This is thorough and goes well beyond what the schema could 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?

The description is a compact paragraph that front-loads the core purpose and then efficiently lists behavioral details. Every sentence contributes new information without redundancy. It is slightly longer than strictly necessary but remains focused and well-structured.

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 the tool's complexity (5 parameters, no output schema, multiple data sources), the description is quite complete. It covers the data source selection, labeling, sharing, and error handling. It does not describe the return format or pagination, but no output schema exists, so the agent is left to infer the response shape. Minor gaps exist but are not critical for basic invocation.

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 100%, so the baseline is 3. The description adds minimal extra meaning—only that the follower range is passed to the Apify actor, which is already implied by the schema. It does not clarify the limit parameter or other nuances beyond what the schema already states.

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 tool searches micro-KOLs by platform, niche, and follower range, with a specific verb and resource. It distinguishes itself from siblings (score_fit, draft_email) by its search-oriented purpose and data sources. The opening sentence is direct and 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 explains the internal data source fallback logic (cache → pool → Apify) but does not explicitly tell an agent when to choose this tool over siblings or when not to use it. It implies usage through its purpose but lacks direct alternatives or exclusion criteria. The sibling names suggest distinct functions, but no explicit routing guidance is given.

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