ms-graph-mcp

people_get

Read-onlyIdempotent

Retrieve a colleague's profile by exact email address, including job title, department, office location, and phone numbers. Ideal when the email is already known.

Instructions

Look up one person in the organisation by their exact email address and return their profile: display name, job title, department, office location and phone numbers. Use when the address is already known — people_search is the tool for finding someone by partial or approximate name. Fails if the address does not belong to a tenant account. Requires User.Read.All.

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": {
      "email": "<email>"
    },
    "name": "people_get"
  }
}

Input Schema

Name	Required	Description	Default
`email`	Yes	The person's email address

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 5, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare this as read-only, open-world, idempotent, and non-destructive. The description adds useful behavioral context by specifying the required permission (User.Read.All), the failure condition for non-tenant addresses, and the exact fields returned, which goes beyond the annotation metadata.

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 description is compact yet packed with essential information: what it does, when to use it, the alternative, failure behavior, and permission requirement. No unnecessary words; every sentence earns its place.

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?

For a simple one-parameter tool with no output schema, the description is remarkably complete. It covers the return content, usage context, failure mode, and permission prerequisites, leaving little ambiguity about the tool's behavior in typical scenarios.

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?

The schema has 100% parameter coverage with a simple 'email' parameter. The description enriches this by clarifying that the email must be exact, that it refers to a person in the organization, and that non-tenant addresses will cause a failure. This adds meaning beyond the schema's basic description.

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 clearly states the tool's function with a specific verb ('Look up') and resource ('one person in the organisation by their exact email address'), and lists the output fields. It explicitly distinguishes itself from the sibling tool people_search, making its purpose unambiguous.

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?

The description gives explicit usage guidance: 'Use when the address is already known' and names the alternative for partial/approximate searches (people_search). It also notes failure conditions and permission requirements, which helps the agent decide when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.