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Microsoft Graph MCP Server

Microsoft Graph MCP Server

by oliverhruby
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

people_search

Search for people in your organization using a query, with results ranked by your interaction and relevance. Locate colleagues quickly through the Microsoft Graph relevance graph.

Instructions

Search people using the relevance graph (colleagues ranked by interaction).

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": "people_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
topNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It usefully reveals that results are ranked by interaction via the relevance graph, which is meaningful context. However, it does not state the output shape, read-only nature, pagination behavior, or any other operational traits.

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 a single dense sentence with no filler. The core action ('Search people') is front-loaded, and the parenthetical about ranking adds relevant context without bloat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no annotations, no output schema, and no parameter descriptions, so the description needs to compensate substantially. It does not mention return values, pagination, sorting details beyond 'ranked by interaction', or any caveats about the search behavior, leaving important information to inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain what 'query' or 'top' mean beyond the bare schema titles. It only implies that a query is used for searching, and adds no detail about query format, top limits, or result behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Search'), a clear resource ('people'), and a distinctive mechanism ('relevance graph'), which is enough to understand the tool's core purpose. It does not explicitly name any sibling tool to distinguish it from, so it misses the full 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool versus alternatives like search_query, list_users, or get_user. The intended use is only implied by the tool name and the phrase 'Search people', with no exclusions or comparison to sibling tools.

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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