payer-api-directory

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

With no annotations, the description carries the burden of behavioral disclosure. It adds specific output details (dated+confidence-tagged pricing signals, confidence enum values) that inform the agent about data structure. It does not mention auth, error behavior, or side effects, but as a get operation the read-only nature is implied; the added confidence enum provides useful scope beyond a generic 'returns record'.

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. It front-loads the primary purpose ('Full record for one vendor') and then efficiently enumerates contained data and clarifies the confidence enum. No wasted words.

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 one-parameter get tool without an output schema, the description sufficiently covers the return content and the confidence value set. It could mention the source of slugs or error handling for invalid slugs, but these are minor gaps; overall it is complete enough for effective use.

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 100% (slug is fully described with examples). The tool description adds no extra meaning to the parameter beyond what the schema already provides, so the baseline 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 clearly states this tool returns the full record for a single vendor, with specific content areas (what it does, pricing signals, access requirements, fit). It contrasts with sibling tools like list_clearinghouses (plural) and recommend_clearinghouse, making its singular resource focus distinctive.

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 phrase 'Full record for one vendor' gives clear context that this is for retrieving detailed information about a single vendor, implying it should be used when you need depth rather than a list. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.

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. It discloses the content of the response (vendor attributes) and implies a read-only listing, but it does not explicitly state that it is safe/read-only, nor mention any pagination, sorting, or authentication requirements. This is acceptable but not comprehensive.

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, well-structured sentence that front-loads the action and resource, then concisely enumerates the returned fields. Every word adds value, with no redundancy or fluff.

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 simplicity (0 params, no output schema, no annotations), the description adequately covers what the tool does and what data it returns. It could be more explicit about return format (e.g., 'returns a list'), but for a list-all tool of this complexity, it is mostly 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 schema has zero parameters, which is confirmed by the description's 'List all' scope. The description clarifies that no filtering is needed, aligning with the empty schema. Baseline for 0 params is 4, and the description adds meaning by indicating the full set is returned without filters.

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 ('List all medical clearinghouse / payer-API vendors'), the scope ('in the directory'), and the returned fields (access model, API style, supported X12 functions, pricing public). This distinguishes it from siblings like get_clearinghouse (specific vendor) and recommend_clearinghouse (recommendation-based).

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 getting a full directory listing but does not explicitly state when to use it versus alternatives. No alternative tools are mentioned or exclusions given. Usage is inferred from the word 'all' rather than stated as guidance.

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 burden. It discloses that ranking is 'transparent' and that it returns reasoning for each vendor, which adds behavioral context. However, it does not mention side effects, data sources, or any potential safety considerations, leaving room for more transparency.

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 two sentences, front-loaded with the core purpose and followed by input semantics. Every sentence 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?

For a simple tool with three optional parameters and no output schema, the description covers purpose, output, and input meanings. It is complete enough for an agent to select and invoke the tool correctly, though it could detail scoring methodology or edge cases if those were important.

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 has 0% description coverage, but the description explicitly explains the meaning of all three parameters: 'functions needed', 'dev-integration flag', and 'budget-sensitive flag'. This adds semantic value beyond the raw schema and helps the agent map inputs to intent.

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 ranks vendors for a practice, using a specific verb ('rank') and resource ('vendors'). It distinguishes itself from sibling tools like get_clearinghouse, list_clearinghouses, and vendors_by_function by emphasizing scored shortlist and reasoning output.

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 context is clear: use this tool when you need a ranked, scored shortlist of vendors with reasoning. It does not explicitly mention alternatives, but the purpose and output description sufficiently imply when this tool is appropriate, and nothing suggests exclusions.

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 burden of behavioral disclosure. It implies a read-only query operation ('Which vendors support...') and limits inputs to a defined list, which adds some transparency. However, it does not disclose details like return format, empty result behavior, or invalid input handling, which would be richer in the absence of annotations.

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 two-sentence structure that front-loads the core purpose and then provides the enumerations. Every word earns its place; there is no padding or repetition.

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 tool with one parameter and no output schema, the description covers the main semantics: what it does and what valid inputs are. It could be slightly more complete by stating what the output looks like (e.g., list of vendor names/IDs), but the implied behavior is clear enough for this simplicity.

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 merely defines 'function' as a string without any description or enum. The description compensates fully by listing the five valid values: eligibility, claims, era, claim-status, prior-auth. This adds essential meaning beyond the schema and guides correct invocation.

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 function: to identify vendors supporting a given X12 function. It lists the exact allowed function slugs, making the purpose even more precise. This distinguishes it from sibling tools focused on clearinghouses.

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 provides clear context for when to use the tool—when you need to know vendor support for a specific X12 function. It also enumerates the valid function values, which is a practical usage guideline. It doesn't explicitly mention alternatives or exclusions, but the context is sufficient.

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