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American Default Research

by org.americandefault
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search_indicators

Read-onlyIdempotent

Search the 103-indicator registry by keyword. Returns ranked matches (up to limit, default 10, max 50) with slug, branded name, underlying name, category, and canonical URL. Scoring is substring+prefix over slug, branded_name, name, and category — e.g. query 'savings' returns both The Buffer (personal saving rate) and The Safety Net (emergency savings survey). Use this when you want to discover which slug corresponds to a concept before calling get_indicator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax number of ranked matches to return. Default 10. Hard cap 50 (values above are clamped). Values above 1000 are rejected as abuse.
queryYesThe keyword to search (e.g., 'savings', 'mortgage', 'inflation'). Scoring runs substring + prefix matching over slug, branded_name, name, and category fields.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"Max number of ranked matches to return. Default 10. Hard cap 50 (values above are clamped). Values above 1000 are rejected as abuse."
    • addedInput schema / properties / query / description
      Added value: +"The keyword to search (e.g., 'savings', 'mortgage', 'inflation'). Scoring runs substring + prefix matching over slug, branded_name, name, and category fields."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral details: ranking mechanism (substring+prefix), default limit and max, and the exact fields returned. This goes well beyond the annotation baseline.

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 sentences, front-loaded with the primary action, followed by return details, scoring explanation, and usage guidance. Every sentence contributes; no filler or repetition.

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 search tool with no output schema, the description fully covers what is returned, how results are ranked, and how to use the result (get_indicator). Combined with the schema and annotations, this is a complete picture.

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% and both parameters are well-documented. The description adds extra semantic value by explaining the scoring fields and the example, which enriches understanding of the query parameter beyond the schema description.

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 the tool searches a specific resource (103-indicator registry) and returns ranked matches. It explicitly differentiates from the sibling get_indicator by framing this as the discovery step before fetching a specific indicator.

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?

Provides explicit usage context: 'Use this when you want to discover which slug corresponds to a concept before calling get_indicator.' This tells the agent exactly when to choose this tool over the sibling, and the example clarifies what kind of queries work.

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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