Returns structured facts about the Taki Dent clinic: legal identity, address, contact details, languages spoken, and published certifications/licences/registrations.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| meta | Yes | ||
| evidence | Yes |
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.
Add one secure layer between your agents and this server.
Each get_* tool targets a distinct information domain (clinic profile, doctor profile, prices, reviews, etc.), but the generic 'fetch' overlaps with the specific getters, creating minor ambiguity.
Most tools follow a get_* pattern, but 'fetch' breaks the convention, and 'search' and 'request_callback' deviate from the get_ prefix, resulting in an inconsistent style.
With 12 tools, the set is well-scoped for a patient services server, covering information retrieval and contact actions without bloat.
The server covers the core patient journey: researching treatments, clinic info, prices, reviews, visit logistics, and initiating contact via callback or WhatsApp. A potential gap is direct appointment booking, but the contact mechanisms compensate.
