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people-context

people-context

by JinyangWang27
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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resolve_person

Read-only

Resolve a name, nickname, or partial reference to ranked candidate people with match scores and reasons, flagging ambiguous results for clarification.

Instructions

Resolve a name, nickname, or partial reference to candidate people.

Call this first whenever the user mentions someone, before asking who they mean. Returns ranked candidates with a score and match reason. If two or more candidates are close, the result is flagged ambiguous and all are returned so you can disambiguate with extra context or a clarifying question. An empty candidate list means no confident match — use remember or remember_person to create a new record. Put distinguishing context in hints (org, role, relationship) rather than in query.

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_person"
  }
}

Input Schema

NameRequiredDescriptionDefault
hintsNo
limitNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.5.0
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "resolve_personDictOutput",
-  "type": "object"
-}New value: +null
  2. First observedv1.1.1

TDQS

A4.9/5.0
Behavior5/5

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

With readOnlyHint already covering the safety profile, the description adds substantial behavior: ranked output with score and match reason, an `ambiguous` flag when candidates are close, and the meaning of an empty list. It even prescribes the disambiguation strategy (extra context or a clarifying question), which is not derivable from annotations.

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?

The guidance is front-loaded: what it does, when to call it, what comes back, how to handle ambiguity, and how to place hint context. Every sentence adds decision-relevant information with 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?

There is no output schema, and the description compensates by describing the return shape (candidates, score, match reason, ambiguous flag, empty list) and the disambiguation path. For a three-parameter read-only resolver, an agent has everything needed to call it correctly and interpret the result.

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 0%, so the description carries the burden, and it does explain the `hints` sub-fields (org, role, relationship) and instructs that distinguishing context belongs there rather than in `query`. The `query` notion is clarified as name/nickname/partial reference, but `limit` (default 5) is never mentioned, leaving one of three parameters undocumented.

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 opens with a precise verb and resource ('Resolve a name, nickname, or partial reference to candidate people') and scopes the input types it accepts. It also separates this resolution step from record-creation siblings (remember, remember_person), so an agent can place the tool in the workflow without opening the 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?

It gives an explicit trigger ('Call this first whenever the user mentions someone, before asking who they mean') and an explicit fallback when the candidate list is empty, naming remember/remember_person as the alternative. The only minor gap is that no distinction is drawn against search_people or semantic_search, but the when-to-use instruction is otherwise unambiguous.

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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