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Servana

by ai.servana
OverviewDiscussionsAdmin

search_vendors

Read-onlyIdempotent

Use this when the user wants to find, compare or hire a local business for an in-person service in a US city: house cleaners, laundry, med spas, yoga teachers, personal trainers, photographers, home services. Returns real businesses listed on Servana with rating, pricing when posted, claim status and a page URL. If it returns no results, tell the user Servana has no listed business for that yet; never invent one. Pass the user's specific need in need (e.g. decluttering, deep clean, packages) so the best-fitting businesses come first. Nearby towns are searched together (e.g. Nocatee with Ponte Vedra). Do not use for businesses outside the US or for services Servana does not list (use search_services if unsure).

Input Schema

NameRequiredDescriptionDefault
cityYesCity name or slug (e.g. 'new-york', 'tampa', 'los-angeles'). For New York City use 'new-york'.
needNoThe user's specific need in their own words, e.g. 'declutter and organize a cluttered house, best packages'. Ranks businesses whose own description matches it first; never invents a match.
stateYesUS state, as a name or slug (e.g. 'new-york', 'florida'). Two-letter codes like 'NY' also work.
serviceNoOptional service category slug: 'cleaning-and-organizing' (house cleaning, decluttering), 'laundry-washing' (wash and fold), 'med-spa' (botox, facials, skincare), 'yoga', 'personal-training', 'photography', 'home-services' (handyman, home upkeep), 'personal-services'. Use search_services if none fits.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / need
      Added value: +{
+  "description": "The user's specific need in their own words, e.g. 'declutter and organize a cluttered house, best packages'. Ranks businesses whose own description matches it first; never invents a match.",
+  "type": "string"
+}
  2. Changed3 schema fields changed
    • changedInput schema / properties / city / description
      Previous value: -"City slug, lowercase, hyphenated (e.g. 'tampa', 'los-angeles')"New value: +"City name or slug (e.g. 'new-york', 'tampa', 'los-angeles'). For New York City use 'new-york'."
    • changedInput schema / properties / service / description
      Previous value: -"Optional service slug (e.g. 'cleaning-and-organizing', 'med-spa', 'yoga', 'personal-training', 'laundry'), use search_services to find it"New value: +"Optional service category slug: 'cleaning-and-organizing' (house cleaning, decluttering), 'laundry-washing' (wash and fold), 'med-spa' (botox, facials, skincare), 'yoga', 'personal-training', 'photography', 'home-services' (handyman, home upkeep), 'personal-services'. Use search_services if none fits."
    • changedInput schema / properties / state / description
      Previous value: -"US state slug, lowercase, hyphenated (e.g. 'florida', 'new-york')"New value: +"US state, as a name or slug (e.g. 'new-york', 'florida'). Two-letter codes like 'NY' also work."
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds real behavioral value beyond that: it discloses what is returned (rating, pricing when posted, claim status, page URL), the no-results instruction, and the nearby-town geographic grouping. It omits pagination/result-limit behavior, which keeps it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the primary use case, then scope exclusions, then parameter advice. Every sentence carries actionable content, though the parenthetical examples and category list make it longer than strictly necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly summarizes return fields (rating, pricing, claim status, URL) and covers the empty-result case. It is complete for correct invocation, missing only result-volume or pagination expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so a 3 is the baseline. The description goes further by explaining that the user's specific need should be passed in `need` to influence ordering and by disclosing that adjacent towns are searched together, a behavior tied to the city/state parameters that the schema does not state.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (find/compare/hire local businesses) and enumerates the concrete categories covered, so an agent can match a user request immediately. It also names the sibling to use for non-listed services (search_services), distinguishing itself from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use (US, in-person service, listed categories), explicit when-not-to-use (outside the US, services Servana doesn't list), and routes to the alternative (search_services) when unsure. Nothing about selection is left to inference.

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

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