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Inspect a Graph resource shape

graph_schema
Read-onlyIdempotent

Discover available properties and JSON types for any Microsoft Graph path by fetching a sample item, so you can build precise $select and $filter parameters.

Instructions

Fetches one item from a Graph path ($top=1) and reports the property names it carries with their JSON types, plus the @odata.context that names the resource type. Use it before graph_request to learn what $select and $filter can reference. It reports the shape of one real item, so properties that happen to be null on that item are absent from the list, and an empty collection yields no properties at all. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
versionNoGraph endpoint to probe. Defaults to the server default (v1.0).
entityPathYesGraph-relative path to a collection or a single entity, e.g. "/me/messages", "/me/drive/root/children" or "/me". Must begin with "/".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish read-only/idempotent/non-destructive behavior. The description adds meaningful behavioral detail beyond that: it fetches only one item via $top=1, reports the shape of a real item, omits null properties, and returns no properties for an empty collection. This helps set expectations about incomplete results.

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?

The description is four sentences and front-loads the core function in the first sentence. The caveats about null properties and empty collections are necessary and earn their place. The final 'Read-only' is redundant with annotations but not harmful.

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 two-parameter probe tool with no output schema, the description covers what the tool returns, how it behaves with real data, and when to use it relative to graph_request. Nothing an agent needs to invoke it correctly is missing.

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 the schema already documents both parameters. The description mentions the Graph path concept and $top=1 but does not add significant parameter-level meaning beyond what the input schema provides. Baseline 3 is appropriate.

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 names a specific verb and resource: it fetches one item from a Graph path and reports property names, JSON types, and @odata.context. It also explicitly positions itself against graph_request by saying it is meant to be used before that tool to learn what $select and $filter can reference.

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?

The description gives clear context: 'Use it before graph_request to learn what $select and $filter can reference.' This names the alternative tool and the condition for using graph_schema. It does not explicitly state when not to use it, but the intended workflow is clear.

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