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estimate_service_request

Read-onlyIdempotent

Use this when the user asks what a local service costs in a city (e.g. 'how much is a deep clean in NYC?'). Returns a rough range from Servana businesses' posted rates with a confidence level ('high' = 5+ local priced businesses, 'medium' = 2-4, 'low' = 0-1, then a category-wide median) and an explanation naming the sample. Always call it an estimate, never a quote: each business sets its own price. Do not use for one business's prices; use get_vendor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesCity name or slug (e.g. 'new-york', 'tampa', 'los-angeles'). For New York City use 'new-york'.
hoursNoOptional: estimated hours needed, if known. Overrides job_size for hourly-rate services.
stateYesUS state, as a name or slug (e.g. 'new-york', 'florida'). Two-letter codes like 'NY' also work.
serviceYesService 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'
job_sizeNoRough size of the job. Defaults to 'medium'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / city / description
      Previous value: -"City slug, lowercase, hyphenated (e.g. 'tampa')"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: -"Service slug, use search_services to find it (e.g. 'cleaning-and-organizing')"New value: +"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'"
    • changedInput schema / properties / state / description
      Previous value: -"US state slug, lowercase, hyphenated (e.g. 'florida')"New value: +"US state, as a name or slug (e.g. 'new-york', 'florida'). Two-letter codes like 'NY' also work."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), and the description adds behavior the annotations cannot: the confidence tiers and their sample-size thresholds, that a category-wide median is used, and the hard constraint to label output an estimate rather than a quote.

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 triggering user utterance, then return shape, then the labeling constraint and the sibling exclusion. Dense but every clause earns its place; the confidence-level parenthetical is the only slightly heavy passage.

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

Completeness5/5

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

There is no output schema, so the description must carry return semantics, and it does: a rough range, a confidence level with defined thresholds, and an explanation naming the sample. Combined with full schema coverage, an agent has everything needed to call and interpret this tool.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters (including the city/state slug conventions and the hours-vs-job_size precedence) are already documented in the schema. The description adds only an illustrative query, not new parameter meaning, so the baseline 3 applies.

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 (estimate) and resource (local service cost in a city) with a concrete example query. It explicitly distinguishes itself from the sibling get_vendor, so an agent can route without opening either schema.

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 ('when the user asks what a local service costs in a city') and when-not ('Do not use for one business's prices; use get_vendor'), naming the alternative tool. Nothing 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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