Phos Sales Engine

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

Since no annotations are provided, the description carries the full burden of behavioral transparency. It reveals that the tool uses web scrapers and deduplicates against an existing database, which adds useful context. However, it does not mention potential rate limits, data freshness, or whether the operation is strictly read-only, leaving some ambiguity about 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?

The description is exactly two sentences, with the first delivering the core purpose and the second adding data-source and deduplication context. Every word earns its place; there is no redundancy or filler.

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 discovery tool with no output schema, the description adequately covers what it does, where it operates (NZ), how it works (Yellow Pages and other scrapers), and a key behavior (dedup against existing DB). It does not spell out the return format, but the tool name and purpose strongly imply a list of prospect businesses, making it sufficiently complete.

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 input schema provides 100% coverage with descriptions for all four parameters (industry, location, api_key, limit). The description only repeats 'industry and location' without adding any new semantic detail, such as syntax, formatting, or parameter interaction, so it does not go beyond the schema.

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 'Find business prospects by industry and location in NZ' – a specific verb, resource, and geographic scope. The mention of 'Uses Yellow Pages and other scrapers to discover real businesses' differentiates it from the sibling tools score_prospect and generate_outreach, which are about scoring and outreach 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?

The description implies this tool is used for discovering prospects by industry and location, but it does not explicitly state when to use it versus alternatives like score_prospect or generate_outreach. No explicit alternatives, exclusions, or prerequisites are mentioned, so the guidance is only implied.

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?

No annotations are provided, so the description carries the full burden. It does disclose the return structure and references 'Claude AI sales brain', indicating AI-driven generation. However, it does not mention potential rate limits, required permissions beyond the api_key parameter, or error behavior for invalid prospect IDs.

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, information-dense sentence that front-loads the action and outcome. It contains no redundant words and 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?

The tool has only 2 parameters and no output schema, so the description compensates by listing the return fields. It could be more thorough by explaining what 'personalization points' contain or any prerequisite steps, but for a simple generation tool it is fairly complete.

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?

Both parameters (api_key, prospect_id) are fully described in the schema (100% coverage), so the schema already documents them. The description adds no additional parameter-level context, meeting the baseline of 3.

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 precisely states the tool's verb ('Generate'), resource ('personalized outreach message for a prospect'), and expected output ('subject, body, personalization points, and alternative subjects'). This clearly differentiates it from sibling tools like find_prospects and score_prospect, which serve different functions.

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 usage on a specific prospect (via prospect_id) and the output details make it clear this is for content generation in a sales workflow. However, it does not explicitly state when to prefer this over siblings or mention exclusion cases.

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?

No annotations are provided, so the description carries the full burden. It adds the useful behavioral trait that the tool is free (0 credits). However, it does not explicitly state that the operation is read-only, what authentication is needed beyond the schema, or the shape of the response. For a getter, the mutation risk is low, but richer disclosures 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 a single, front-loaded sentence stating the purpose, followed by a brief credit note. Every word earns its place with no fluff or redundancy.

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?

The tool is simple with only two parameters and no output schema. The description lists the key statistics returned ('prospects by stage, conversion rates, and daily metrics'), giving a solid picture of the output. It could mention product parameter usage or limitations, but the schema covers parameters, and the core functionality is well captured.

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%: both api_key and product are documented with clear descriptions. The description adds no parameter-specific details beyond what the schema already provides. Per the baseline rule for high schema coverage, a score of 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 uses a specific verb and resource: 'Get pipeline statistics' clearly states the action and target. It also enumerates the contained metrics ('prospects by stage, conversion rates, and daily metrics'), distinguishing it from sibling tools like find_prospects, generate_outreach, and score_prospect.

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 the tool is for retrieving pipeline statistics, which is a distinct operation from the siblings. However, it provides no explicit when-to-use guidance, no exclusions, and no mention of alternative tools. The usage context is inferred but not articulated.

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?

No annotations are provided, so the description must disclose behavioral traits. It states the return value (fit score 0-100 with breakdown and factors), which is useful, but it does not mention side effects, authentication requirements beyond the api_key parameter, or whether the operation mutates data. This leaves some ambiguity, but the read-only nature of scoring is reasonably implied.

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, front-loaded sentence that directly states the action and expected output. Every word contributes value, with no filler or redundancy.

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 no output schema, the description appropriately mentions the return format (score 0-100) and breakdown, but it does not elaborate on what 'contributing factors' include or provide usage prerequisites. It is adequate for a straightforward scoring tool but leaves a few details to inference.

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 schema already provides descriptions for both api_key and prospect_data, and the nested fields are self-explanatory. The tool description adds no additional parameter-level detail, so it does not significantly exceed the schema's coverage, matching 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 uses the specific verb 'Score' and identifies the resource ('a prospect') and the criterion ('against your Ideal Customer Profile'). This clearly distinguishes it from sibling tools like find_prospects, generate_outreach, and get_pipeline, which serve different actions.

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 the tool is for scoring prospects against an ICP, but it does not explicitly state when to use it versus the sibling tools or provide any exclusions or alternative guidance. However, the distinct action makes the use case fairly obvious.

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