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TheAIMeters

search_meters

Read-onlyIdempotent

Find meters when the exact slug is unknown. The backend searches for a literal, case-insensitive substring in slug, label, group and sourceNote, and orders matches by slug. This is not semantic search; no matches returns an empty meters array. Use list_meters to browse all meters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNon-empty search text, for example water or GPU. Percent and underscore are literal characters, not wildcards.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metersYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Goes well past the safety annotations by disclosing the matching algorithm (literal, case-insensitive substring), the searched fields, the result ordering, that it is not semantic search, and that no matches yields an empty meters array. With readOnlyHint/idempotentHint already covering safety, this is meaningful added context.

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?

Four tight sentences, each carrying distinct information (purpose, matching behavior, negative case, alternative). Front-loaded with the selection condition before the mechanics.

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?

An output schema exists, so return-shape detail is optional, yet the description still adds the empty-result behavior. For a one-parameter read tool with full schema coverage and annotations, nothing an agent needs to call it correctly is missing.

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 the schema already documents the single query parameter including literal percent/underscore handling, so the baseline is 3. The description adds genuine semantics beyond the schema: literal case-insensitive substring matching across slug, label, group and sourceNote, plus ordering by slug.

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 meters') and immediately qualifies the scenario ('when the exact slug is unknown'). It contrasts itself against list_meters, so an agent can distinguish it from siblings without opening a 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?

Explicitly frames the condition for use (slug unknown, non-semantic intent) and names the alternative for the opposing case ('Use list_meters to browse all meters'). When-to-use and the alternative are both stated.

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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