Nonlinear Calendar

People roster · 人物花名册

people_roster

This tool reads a user-named people roster. Do not invent people or merge two ids. Same role ≠ same person. Call when the user names a person, asks 这个人是谁 / 西交和宁波是不是同一个 / 花名册 / people roster, or before telling a story that involves named people. Pass people=[{name, relation, id?}] from what the user just said. If empty, ask the user to name people (name + one-line relation) — do not invent a biography. Optional env PEOPLE_ROSTER_JSON when the user keeps a local roster. Not L1. Public · no API key.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "people_roster"
  }
}

Input Schema

Name	Required	Description	Default
`locale`	No	Reply language. zh (default) or en.
`people`	No	This-turn roster. Wins over PEOPLE_ROSTER_JSON. Omit to read the env roster.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 11, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden, and it does disclose real traits: read-only semantics ('reads'), auth profile ('Public · no API key'), env-var fallback behavior, and data-integrity guardrails (don't merge ids, same role ≠ same person, don't invent biographies). It stops short of describing what the read returns or how errors surface, and 'Not L1' is unexplained jargon.

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?

Purpose is front-loaded and every sentence is load-bearing, but the anti-hallucination rule is repeated three times (don't invent people / don't invent a biography / same role ≠ same person), and the opaque 'Not L1' fragment adds noise rather than signal.

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?

For a read-only, no-auth, zero-required-parameter tool with no output schema and full schema coverage, the description covers selection criteria, guardrails, env behavior, and auth. The main remaining gap is any hint of what a successful read returns.

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%, so both parameters are already documented, including precedence ('Wins over PEOPLE_ROSTER_JSON'). The description restates the people shape and the env fallback rather than adding syntax or format meaning, so the baseline 3 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?

Opens with a precise verb+resource: 'reads a user-named people roster.' No sibling tool (activity_window, body_rhythm, etc.) does roster reads, so disambiguation is inherent, and the roster scope is stated unambiguously.

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?

Gives explicit call triggers with concrete example queries in both languages (这个人是谁 / 花名册 / people roster; before telling a story with named people) and an explicit negative case: if the roster is empty, ask the user to name people rather than proceeding. Nothing is left 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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