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chrischall

angi-mcp

by chrischall

angi_list_trades

Read-onlyIdempotent

Get every Angi trade slug, from plumbing to basement waterproofing, to map free-text trades to the exact slugs required for searching pros. Reads the public sitemap directly.

Instructions

List every trade slug Angi publishes (~312, e.g. "plumbing", "air-duct-cleaning", "basement-waterproofing"). Call this to resolve a free-text trade to the slug angi_search_pros needs. Reads Angi's public sitemap directly — no browser bridge required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
containsNoCase-insensitive substring filter, e.g. "duct" or "roof".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.4.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, and idempotent behavior. The description adds useful context beyond that: it reads Angi's public sitemap directly, requires no browser bridge, and lists approximately 312 entries. This gives the agent an accurate mental model of the operation.

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?

The description is compact and front-loaded: it states the output, gives examples, explains the use case, and notes the data source in three sentences. Every sentence earns its place with no redundant wording.

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?

For a tool with one optional parameter and strong annotations covering safety and idempotency, the description provides everything needed: what it returns, why to call it, how it works, and what it does not require. No output schema is needed since the return type (a list of slug strings) is directly inferable.

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 100%, with the only parameter 'contains' already documented as a case-insensitive substring filter. The description reinforces the use case but does not add significant new parameter-level meaning beyond what the schema provides, so the baseline 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?

The description clearly states a specific action and resource: 'List every trade slug Angi publishes' with concrete examples. It also names the downstream consumer (angi_search_pros), which distinguishes this tool from sibling tools dealing with reviews, cities, or pros.

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?

The description explicitly says when to call it: 'resolve a free-text trade to the slug angi_search_pros needs.' It does not explicitly mention when not to use it or compare it to alternatives, but the intended usage context is clear enough to guide an agent.

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