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client-compass

List People

list_people

Retrieve all people (clients) for the authenticated user. Optionally search by name. Returns contact details only, not the Lowdown: for any read, card or "go deeper", call generate_compass.

Input Schema

NameRequiredDescriptionDefault
paceNoFilter by preferred pace (e.g. quick, measured, deliberate)
toneNoFilter by tone (e.g. confident, collaborative, analytical)
voiceNoFilter by communication voice (e.g. direct, warm, conversational)
searchNoSearch by name (partial match, case-insensitive)
archetypeNoFilter by archetype name

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / archetype / description
      Previous value: -"Filter by behavioral archetype"New value: +"Filter by archetype name"
    • removedInput schema / properties / archetype / enum
      Removed value: -[
      -  "Initiator",
      -  "Connector",
      -  "Creator",
      -  "Operator",
      -  "Maverick",
      -  "Guardian",
      -  "Navigator",
      -  "Architect",
      -  "Advocate",
      -  "Lighthouse",
      -  "Blueprint",
      -  "Mentor"
      -]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the disclosure burden, and it does well: it clarifies the result set is limited to the authenticated user's people and that only contact details are returned. It omits pagination, ordering, and limits, which matters for a list operation.

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?

Three short sentences, front-loaded with the core purpose and scope. The closing routing sentence is slightly jargon-heavy ('the Lowdown', 'card', 'go deeper') but still carries actionable guidance.

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?

With no output schema and no annotations, the description supplies the essential context: scope, return content, and the retrieval alternative. It falls short on result-set behavior such as pagination or ordering, which an agent might need for a list tool.

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 description coverage is 100%, so all five filter parameters (pace, tone, voice, search, archetype) are already documented in the schema. The description only restates the name-search behavior, adding no syntax or semantic value beyond structured data.

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 ('Retrieve all people (clients)') and scopes it to the authenticated user. It also explicitly names what it does NOT return ('not the Lowdown') and points to the sibling generate_compass, so an agent can distinguish it from related tools.

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?

Explains the optional search-by-name path and routes 'any read, card or go deeper' requests to generate_compass. It does not mention get_person as the single-record alternative, leaving one obvious sibling comparison to inference.

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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