patient-services

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

Annotations already declare readOnlyHint=true and destructiveHint=false; the description adds the scope constraint 'returned by search' and 'full published record' but no additional behavioral details such as error handling or auth requirements. No contradiction.

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 focused sentences: first states the action and scope, second explains the parameter interchangeability. No redundant 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?

Output schema covers return structure; annotations cover safety; description provides the essential usage context. It stops short of explicitly contrasting with sibling get_* tools, but the 'full published record' phrasing and sibling names make the distinction clear.

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% with descriptions for both id and entity_id. The description reinforces that either parameter can be used and that the id originates from a search result, but adds little beyond the schema descriptions.

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 'Retrieves' with resource 'full published record for an entity returned by search' and enumerates entity types, clearly distinguishing from sibling get_x tools that retrieve specific aspects.

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 positions the tool as the follow-up to search, instructing to pass the search result's id. It does not name alternatives or exclusions, but the scope (full record vs. specialized getters) provides clear context.

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 destructiveHint=false, so the description doesn't need to repeat safety. The description adds no additional behavioral details beyond the content returned, but it doesn't contradict annotations. This is adequate given the annotation coverage.

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 front-loads the verb 'Returns' and clearly specifies the content. Every word earns its place 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?

This is a simple read-only tool with no parameters, full annotations, and an output schema. The description accurately covers the tool's purpose without missing critical information, making it complete for an agent to invoke 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?

The tool has zero parameters, so the description doesn't need to explain parameter formats. Per the rubric, a baseline of 4 is appropriate when no parameters exist.

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 two specific things: the clinic's remote follow-up schedule and the process for handling a suspected problem after a patient returns home. This distinguishes it from siblings like get_visit_information and get_clinic_profile by specifying the aftercare context.

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 for post-discharge scenarios, which is clear from the phrase 'after a patient has returned home.' It does not explicitly name alternatives or exclusions, but the context is specific enough for an agent to determine when to use this tool over others.

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 destructiveHint=false, so the description's added value is scoping the content (e.g., 'published' certifications) and indicating a structured response. This enriches the behavioral profile without contradicting 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?

One sentence, front-loaded with the verb 'Returns' and the resource, followed by a concise list of content categories. Every word earns its place; no 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?

For a zero-parameter, read-only tool with an output schema, the description adequately enumerates all relevant content areas. It is complete for the tool's simplicity and aligns with sibling tools' scope.

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 has no parameter burden. The empty schema is fully covered, and the description's mention of specific data fields adds semantic context beyond the bare 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 the tool's function: returning structured facts about the Taki Dent clinic, listing specific content categories. This is a specific verb + resource combination, and it distinguishes itself from sibling tools like get_doctor_profile or get_treatments by focusing on clinic-level facts.

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 on what the tool covers (legal identity, address, contact, languages, certifications), implying it is the go-to for clinic profile details. It does not explicitly exclude alternatives, but the resource is well-defined.

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 mark the operation as read-only and non-destructive. The description adds valuable context by specifying that it returns published professional memberships and supports name-spelling aliases, going beyond the annotation flags without contradicting them.

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?

A single, information-dense sentence that front-loads the action ('Returns a clinician profile') and enumerates key fields and matching criteria without 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?

The tool has one well-documented parameter, a read-only annotation, an output schema, and a description that covers output fields and matching behavior. Sibling tools provide context for alternatives, making the description complete for a simple profile lookup.

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 fully documents the 'doctor' parameter with examples (entity_id, name, alias, page slug). The description reiterates this but adds no new meaning beyond the schema, so the baseline 3 applies given 100% schema coverage.

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 clinician profile with specific fields (title, specialty, languages, education, memberships). It distinguishes from siblings like get_clinic_profile by focusing on the doctor, and the matching by entity_id, name, or alias confirms the resource.

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 clear usage: when needing a clinician profile, provide an identifier. It mentions accepted input formats, but does not explicitly contrast with alternatives like get_clinic_profile or search. The context is strong but lacks explicit 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is known. The description adds valuable context about response content (tiering, exclusions, remote-claim process) that goes beyond annotations. No contradiction; credit for adding behavioral 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?

The description is a single sentence that is front-loaded with the action and resource, followed by key details. Every clause adds value—no fluff or repetition. Ideal length for a zero-parameter tool.

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 read-only tool with zero parameters and an output schema, the description is complete. It explains what the tool returns, the tiering logic, and additional included information (exclusions, remote-claim). No gaps for the agent to resolve.

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?

There are zero parameters, so schema coverage is trivially 100%. The baseline for 0 params is 4, and the description does not need to explain parameters. It adds no parameter semantics, but that is not required.

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 ('Returns') and clearly identifies the resource (clinic's warranty terms), with detailed specifics (tiered by material, exclusions, remote-claim process) that distinguish it from sibling tools like get_prices or get_aftercare_information. No ambiguity.

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 use when warranty terms are needed, but does not explicitly state when to use this tool versus alternatives or provide exclusions. Context from sibling tool names helps, but the description itself offers no direct guidance or alternative mentions.

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 destructiveHint=false, so the safety profile is covered. The description adds context about the output (published, in GBP/EUR, per treatment/full-mouth package) but does not disclose additional behavioral traits like error conditions or data completeness. This is consistent with the moderate effort seen in similar read-only tools.

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 clearly states the verb and resource, with no filler or redundant information. 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?

Given the tool's simplicity (one optional parameter, read-only annotated, output schema present), the description is fully complete. It specifies the exact scope of results and the output schema handles return-value structure, so no additional context is needed.

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 provides full coverage of the single optional 'treatment' parameter with an example ('all-on-4'). The description does not add any meaning beyond what the schema already specifies, so the baseline 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 a specific verb ('Returns') and resource ('the clinic's published treatment prices') with additional scope details (GBP/EUR, per treatment and per full-mouth package). This clearly differentiates it from sibling tools like get_treatments or fetch.

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 this tool: when needing published treatment prices in GBP/EUR. It does not explicitly name alternatives or exclusions, which would warrant a 5, but the scope is specific enough for an agent to infer appropriate usage.

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 the description adds that the data is 'sourced from the live Google Business Profile', indicating it reflects current live data. No contradictions with annotations, and the addition of the live source is meaningful context beyond the structured metadata.

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 with no fluff, front-loads the action ('Returns'), and includes only essential information about the resource and source. 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?

Given the tool has no parameters, an output schema is present to define return values, and the description covers the key purpose and data source. It is fully complete for a simple read-only getter, and no further elaboration is needed.

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 need not explain parameter semantics. The baseline for no parameters is 4, and the description adds no unnecessary parameter details.

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 the clinic's Google review count and average rating, using a specific verb ('Returns') and resource. It is distinct from sibling tools that cover different information like clinic profile, treatments, or contact details.

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 that the tool is for retrieving review count and rating, implying when to use it. However, it does not explicitly mention alternatives or cases where another sibling tool might be more appropriate, so it lacks explicit 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds the behavioral context that the tool returns canonical page URLs for each treatment, which is useful beyond the annotations. It does not contradict any annotation and provides a bit of extra transparency about the output content.

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 of twelve words, front-loaded with the verb and resource. Every word adds value, and there is no repetition of schema information or filler. It is concisely structured without sacrificing clarity.

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 simplicity (no parameters, a clear read-only list operation), the description is complete. An output schema exists to describe the return structure, so the description does not need to detail return values. It sufficiently covers the purpose and the one key behavioral detail (canonical URL).

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 baseline is 4. The description correctly adds no parameter information because there are none to clarify. The schema coverage is 100% trivially, and the description is consistent with the absence of parameters.

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 'Lists' with the resource 'treatments offered by the clinic' and adds the meaningful detail that each treatment comes with its canonical page URL. This clearly distinguishes it from sibling tools like get_prices or get_aftercare_information, which focus on different aspects of the clinic.

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 through its clear statement of what the tool does, but it does not explicitly state when to use this over alternatives like search or other get_* tools. There are no exclusions or 'instead of' guidance given, so the agent must infer based on the tool name and description.

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 mark this as read-only and non-destructive. The description adds context about the scope of returned data (practical travel facts). It does not disclose any unusual behavioral traits, but none are expected for a simple zero-parameter read-only tool.

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, front-loaded with the main action ('Returns'), and provides a comprehensive but concise list of contents. Every word earns its place with no 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?

With zero parameters and an output schema present, the description sufficiently covers the tool's scope and return content. The only minor gap is not explicitly stating how the clinic is identified, but this is likely implied by shared context across sibling tools.

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 schema coverage is complete. The description adds no parameter information because there are no parameters. Baseline of 4 is appropriate for a no-parameter tool.

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 'Returns' and clearly defines the resource as 'practical trip-planning facts,' then enumerates concrete items (clinic location, opening hours, airport distance, etc.). This distinguishes it from sibling tools like get_clinic_profile or get_prices.

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 use for trip planning by listing relevant facts. It doesn't explicitly name alternatives, but the content clearly differentiates from siblings like get_prices or get_aftercare_information. The context is clear and there are 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by noting the link is 'pre-filled with a consultation-request message in the requested language,' which is not inferable from the schema or annotations. This enriches the agent's understanding of the tool's behavior.

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 verb and object, with no filler. Every phrase earns its place by specifying the return value and the language-dependence of the message.

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 optional parameter, an output schema, and comprehensive annotations, the description is adequately complete. It clearly states what is returned and the key variable (language). It does not mention edge cases like invalid language codes, but the schema's default and existing annotations make this a minor gap.

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 provides 100% coverage for the sole parameter 'language' with a description and default value. The description's mention of 'requested language' aligns with the parameter but adds no new semantic information beyond the schema. Baseline 3 is appropriate given the high schema coverage.

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 ('Returns') and names the exact resource ('WhatsApp contact deep link'), including the pre-filled consultation-request message and language parameter. This clearly distinguishes it from sibling tools like get_clinic_profile or get_visit_information, which return different information.

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 a WhatsApp contact deep link, but it does not explicitly state when to use it over alternatives or provide exclusions. Unlike the high-calibration example, it does not name any sibling tool for comparison or mention 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?

Annotations mark destructiveHint=true, and the description adds 'irreversible once sent', which is specific and useful. It also adds a payload restriction ('do not include medical details'), providing context beyond the annotation. No contradiction with 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?

Two sentences, front-loaded with the primary action, and no redundant words. The key warnings are included without fluff, making it highly concise 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?

For a tool of this complexity (5 params, destructive annotation, output schema present), the description covers core behavior, payload limits, and irreversibility. Since an output schema exists, return values need not be explained. It lacks details about next steps but is otherwise sufficient for selection and 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 low (20% – only 'consent' has a description). The description mentions 'name and contact details', which loosely maps to name/email/phone but does not elaborate on each parameter or the preferred_language enum. The schema provides structural info but the description only partially compensates for the low coverage.

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 ('Requests') and a clear resource ('callback from the clinic's patient coordination team'). It distinguishes itself from all sibling tools, which are read-only (get/fetch/search), and clarifies it collects only contact information, not medical details.

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: this is the only mutating tool among read-only siblings, so usage is implied. The description adds a guideline about what to include ('only contact information') and explicitly excludes medical details, but it does not name alternative tools or 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?

Annotations already declare readOnlyHint=true, so the description adds useful context by specifying the entity index scope and supported languages. No contradictions with annotations. It does not cover rate limits or pagination, but those are not necessary given the annotations and output schema.

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 tight sentences that front-load the action and include only essential information: entity types, search fields, languages, and the id-to-fetch linkage. No 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?

Given an output schema exists and annotations handle safety, the description fully covers the search scope, language support, and follow-up workflow. No major gaps for an agent to understand how to invoke this 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 all three parameters, so the baseline is 3. The description does not add parameter-specific semantics beyond the schema; it mentions the search scope and languages but no extra syntax, defaults, or examples beyond what 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?

Clearly states it searches the clinic, doctor, treatment, service, and policy entity index by name, alias, or summary text. This distinguishes it from sibling getter tools like get_clinic_profile and get_prices by being the search entry point.

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?

Explicitly explains that results carry an id that can be passed to the fetch tool, implying a search-then-fetch workflow. It does not explicitly mention when not to use this tool versus get_* tools, but the intended workflow is clear.

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