VetClinic.info — Veterinary Specialty Directory

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

With no annotations, the description carries the full burden. It discloses several behavioral traits: anonymity, deterministic output on identical inputs, PII field rejection, and the nature of the return value (a low–high range). While it could mention more (e.g., error handling), it covers the key operational behaviors.

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 concise sentences with no filler. Each sentence provides distinct value: what it does, its deterministic property, and the PII/alternative note. The structure is front-loaded with the core purpose.

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 nested parameters and no output schema, this description covers the essential context: return format, determinism, PII handling, and distinction from a sibling tool. It does not elaborate on input constraints or error cases, but given the complexity, it is reasonably 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?

Schema description coverage is 0%, so the description must compensate. It explains that the range is computed from 'regional + case factors', implicitly mapping to zip and case_factors, but does not mention procedure_slug. It also notes PII fields are rejected, adding some semantic value. However, given the low coverage, more explicit parameter explanation would be needed for full compensation.

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: providing an anonymous cost estimate with a $low–$high range. It distinguishes itself from sibling tool quote_for_case by explicitly noting the PII rejection, making its purpose 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 explicitly says when to use this tool (anonymous, non-PII inputs) and when to use the alternative (quote_for_case for PII path). This direct comparison provides strong usage 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?

With no annotations provided, the description carries the full burden. It discloses that the tool returns a ranked list and is a convenience wrapper, which implies a read-only operation. However, it does not explicitly state that it has no side effects, nor does it mention pagination or other behavioral details that could be relevant.

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 concise sentences with no unnecessary words. It front-loads the key concept ('Convenience') and immediately explains the tool's behavior, making it easy to scan.

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 simple schema (3 params, no output schema) and the presence of sibling tools, the description adequately explains the tool's purpose and relationship to get_procedure. It does not cover the limit parameter's behavior or return format, but those are inferable from the schema and the 'list' wording.

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 67% (procedure and state have descriptions; limit does not). The description adds only marginal value by clarifying that the result is filtered by state, but it does not elaborate on the 'slug' format or explain the limit parameter beyond what the schema already provides.

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: it combines get_procedure with a state filter and returns a ranked specialist finder list. This specific verb ('Returns') and resource ('ranked specialist finder list') clearly distinguish it from sibling tools like get_procedure.

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 'Convenience' framing implies this tool should be used when you need both a procedure lookup and a state filter together, rather than calling get_procedure separately. However, it does not explicitly state when not to use this tool or mention alternative approaches, leaving some room for interpretation.

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 transparency burden. It discloses the return contents ('credentials, inline reviews, procedures performed, contact action, canonical URL'), which is useful, but it does not explicitly state read-only behavior, error handling, authentication requirements, or any side effects. It is a mild positive but not a full disclosure.

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 immediately states the action and resource, followed by a concise list of contents. Every word earns its place; no filler 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 fetch tool with 100% schema coverage and no output schema, the description adequately covers what the tool does and what it returns. It could explicitly mention error behavior or fallbacks, but given the tool's simplicity, the current description is 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 schema already has 100% coverage, with each parameter described and the slug explanation calling out that slugs are unique per city. The description adds no additional parameter semantics beyond what the schema 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 uses a specific verb ('Fetch') and resource ('a single clinic's full record'), clearly distinguishing it from sibling tools like search_clinics (which searches) and get_procedure (which fetches a procedure). It also enumerates the record's contents (credentials, reviews, procedures, contact action, canonical URL), making the purpose unmistakable.

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 fetching a single clinic when you know its slug, but it does not explicitly state when to use this over alternatives like search_clinics, nor does it mention exclusions or prerequisites. The schema provides helpful disambiguation guidance (pairing slug with city and state), but the description itself lacks direct usage direction.

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 discloses conditional behavior (state triggers a clinic finder) and the article's contents. However, it does not explicitly state whether the tool is read-only, error behavior, or any limitations. Adequate but not rich.

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, front-loaded with the main purpose. Every part is meaningful, no fluff. Excellent structure.

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 relatively simple fetch tool with two parameters, the description is complete enough: it lists the article sections and the conditional clinic finder. Minor gaps include error handling and invalid input behavior, but these are not critical for a read-only retrieval tool.

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% for both parameters. The description adds meaning by explaining that providing state triggers an additional 'ranked clinic finder' sub-component, which is not fully conveyed by the schema's 'pre-filter the finder.' This adds semantic value 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 identifies the tool's purpose: fetching a procedure article with specific sections (cost band, contraindications, emergency signals, FAQ, citations). It distinguishes itself from sibling tools like estimate_procedure_cost, get_clinic, and find_specialists_by_procedure by focusing on the article retrieval.

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 context: use when you need a procedure article. It mentions conditional behavior with state (clinic finder) but does not explicitly state when not to use it or mention alternatives like search_clinics. Clear context, but no 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, the description carries the full behavioral burden. It discloses the live-article filter and the exact returned fields, which adds meaningful context beyond a generic 'list procedures'. It does not mention pagination, ordering, or explicit read-only status, but for a no-parameter list tool the behavior is clear and non-destructive.

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 sentence that leads with the core concept ('Published procedure catalog') followed by the specific return fields and filtering condition. Every word earns its place with 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 simple list tool with no parameters and no output schema, the description is complete. It specifies the scope (published, live articles), the exact fields returned, and the coverage ('every procedure'). It provides all necessary information for an agent to invoke the tool and interpret results.

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 has zero parameters, so the description rightly focuses on output semantics. Baseline for zero-parameter tools is 4. No parameter explanations are needed, and the schema (empty) confirms no inputs.

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 returns a published procedure catalog with specific fields (slug, display_name, also_known_as, specialty_slug) for every procedure with a live article. This is a specific verb+resource combination that is distinct from siblings like get_procedure (single procedure) and list_specialties (specialties).

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 listing all procedures but doesn't explicitly compare to alternatives like get_procedure or search_clinics. It provides context (published catalog, live articles) but lacks explicit when-to-use or when-not-to-use guidance. Usage is implied rather than 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, the description carries the burden and largely meets it by describing output fields and the 'when applicable' qualifier for sameAs. It implies a read-only operation via 'Returns' and defines the scope as a 'closed-set enum', but it does not explicitly confirm no side effects or other behavioral details.

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: the first defines the tool's essence and return structure; the second gives actionable usage context. Every phrase earns its place with no padding 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 no-parameter, no-output-schema tool, the description fully covers what the tool returns, the nature of the data ('closed-set enum'), and when to invoke it. It is self-sufficient and leaves no major gaps for an agent to misuse.

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 has zero parameters, so by the rubric baseline is 4. The description adds no parameter explanation because none are needed.

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 ('Returns') and resource ('specialty slugs'), clearly identifying it as a reference list tool. It distinguishes itself from search_clinics by framing itself as a prerequisite mapping step, and it enumerates exact return fields (slug, display_name, credentialing_board.sameAs).

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?

Provides explicit guidance: 'Always call this before search_clinics if uncertain about specialty mapping.' This gives a clear when-to-use scenario, though it does not mention alternatives beyond search_clinics or explicitly state when not to use it.

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 fully discloses side effects: submitting a confirmation-gated lead, dispatching an email, and returning status='estimate_only' when PII is absent. It also reveals the auth header requirement, making all material behavioral traits explicit.

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 concise sentences front-load the core purpose ('Composite tool. Always computes an estimate.') and efficiently cover conditional behavior, side effects, and a needed header. No redundant wording; every clause contributes.

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?

Despite no output schema, the description covers conditional paths, side effects, return status, and a required header. It references a sibling tool to fully specify behavior when PII is absent, making the tool's operation clear even without an output 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 coverage is high (71%), but the description adds crucial meaning beyond the schema: owner_email triggers the write path and requires X-Agent-Attribution, and case_factors is defined by reference to estimate_procedure_cost. This provides semantic context not present in the schema, though not every parameter is elaborated.

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 computes an estimate and, when owner_email is present, additionally submits a lead and sends a confirmation email. It distinguishes itself from the sibling estimate_procedure_cost by noting identical behavior when PII is absent, making the tool's dual-mode purpose 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 explicitly contrasts behavior with estimate_procedure_cost ('behaves identically' without PII) and identifies the condition (owner_email present) that triggers the lead-capture path. It also specifies the prerequisite X-Agent-Attribution header, giving clear when-to-use 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 exist, so the description carries the burden; it discloses ranking behavior (promoted list first), return fields, and the disambiguation error with data.candidates, but omits auth/rate limits and pagination details.

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?

Four concise sentences front-load the purpose and key behaviors with zero 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 search tool with no output schema, it adequately describes return shape and the ambiguity error pathway, though it lacks notes on pagination or auth requirements.

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 already covers 75% of params with descriptions; the description adds value by explaining the genuinely-optional state behavior and the error-response re-call nuance, but doesn't fully clarify limit semantics beyond 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?

Clearly states it finds veterinary specialty clinics by city+specialty, lists return fields (credentials, address, rating, review count, contact action), and distinguishes from sibling tools like get_clinic (single clinic) and find_specialists_by_procedure (by procedure).

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?

Provides clear search context (city+specialty) and advises how to handle optional state, but doesn't explicitly name alternatives or when-not-to-use scenarios relative to sibling tools.

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