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Local Pros by Ron Ventures

request_quote

Send a quote request to one of the businesses returned by find_local_pros. The business gets an email with the customer's name, contact and problem and replies directly. Ask the user for permission before calling this: it shares their name and contact with the business.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe business slug from find_local_pros.
contactYesThe customer's phone number or email for the business to reply to.
problemYesWhat needs doing, where (city/neighborhood) and when.
customer_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the side effect (the business is emailed with name, contact and problem), that the business replies directly to the customer, and that invoking it shares the user's personal data — a privacy disclosure an agent needs. It stops short of stating auth requirements, failure modes, or idempotency, but the material behavior is covered.

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

Conciseness5/5

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

Three short sentences, zero filler: purpose first, mechanism second, safety precondition last. Each sentence earns its place.

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?

For a side-effecting tool with no annotations and no output schema, the description covers the essentials: what is sent, to whom, and the permission requirement. It leaves unspecified what the tool returns on success and how errors surface, which is a minor gap but the agent has enough to call it correctly.

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 coverage is 75% and the four fields are largely self-describing, so baseline is 3. The description names three of the four inputs (name, contact, problem) in prose but adds no format, constraint, or validation detail beyond what the schema and its field descriptions already provide.

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 ('send a quote request') and pins the target to businesses 'returned by find_local_pros', which uniquely distinguishes it from its only sibling. An agent knows exactly what this does and where its input comes from without opening the schema.

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

Usage Guidelines4/5

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

Gives a clear prerequisite chain: call find_local_pros first, then request a quote, and obtain user permission before invoking. It does not state what to do if the user declines or whether repeat requests are allowed, but the core when-to-use context is explicit.

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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