Quattr

resolve_entity

Read-only

Resolve a customer's named business objects — segments, AI trackers / AI Visibility segment groups, AI platforms, goals, competitor sets — from natural language (a tracker name, segment group name, "default", a platform like Claude, a goal) to the exact filter field and value to use. Call this BEFORE filtering by any entity the user named, so you bind the right id instead of guessing. For an AI-visibility question where the user did not name a tracker / segment group, call with kinds:["ai_tracker"] and a generic query such as "AI Tracker" or "segment group" to show the full tracker picker.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "resolve_entity"
  }
}

Input Schema

Name	Required	Description
`kinds`	No	restrict to these entity kinds
`limit`	No
`query`	Yes	the entity name or fragment the user mentioned, e.g. a tracker / segment group name, "default", or a platform like Claude

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real behavioral value beyond them: it says what is returned (the exact filter field and value), and that a generic query surfaces the full tracker picker. It does not mention result-count behavior or how limit truncation affects the picker.

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?

Two dense sentences that are well front-loaded: purpose first, then the when-to-call rule, then the edge-case pattern. Nothing is padding, though the second sentence packs the fallback pattern into a long clause that takes a second read.

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?

No output schema exists, so the description carries the return-value burden and does so ('the exact filter field and value to use'). With annotations covering safety and 67% schema coverage on parameters, an agent has everything needed to invoke this correctly, including the ambiguous no-name case.

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 description coverage is 67% (both kinds and query are documented, limit is not), so baseline is 3. The description adds meaning beyond the schema by mapping natural-language inputs (tracker name, segment group name, "default", a platform like Claude, a goal) to the abstract query parameter, and by enumerating the entity kinds in prose.

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 (resolve) and resource (named business objects), then enumerates the exact kinds it handles — segments, AI trackers/segment groups, platforms, goals, competitor sets — and the output form (filter field + value). It is clearly distinguishable from every analysis sibling because it is a binding step, not an analysis.

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 instructs 'Call this BEFORE filtering by any entity the user named, so you bind the right id instead of guessing,' which is a clear when-to-use rule. It also gives a concrete fallback invocation (kinds:["ai_tracker"] with a generic query like "AI Tracker") for the specific case where the user named no tracker.

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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