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Microsoft Graph MCP Server

Microsoft Graph MCP Server

by oliverhruby
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

delta_query

Track changes to Microsoft 365 resources such as messages, events, contacts, and drive items by running delta queries with an optional delta token to retrieve only changes since the last sync.

Instructions

Execute a delta query on a resource. resource: 'me/messages', 'me/events', 'me/contacts', 'me/drive/root'.

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": {
      "resource": "<resource>"
    },
    "name": "delta_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
resourceYes
delta_tokenNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations at all, the description carries the full burden of behavioral disclosure. It only names the operation and lists resources; it does not explain pagination, delta token semantics, that results represent changes since the previous call, or whether it is a read-only operation. This is a meaningful gap for a delta query tool.

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 a single efficient sentence with no wasted words, and the resource examples are front-loaded. It earns its place by providing concrete valid values, though it could be structured to include token guidance without bloating.

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?

For a tool with no annotations, no output schema, and two parameters at 0% schema coverage, the description is under-specified. Important context—how to obtain and use delta_token, what the response represents, and how this differs from plain listing—is missing, leaving an agent with significant questions.

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%, so the description must compensate. It does provide useful example values for the resource parameter, but it completely fails to explain the delta_token parameter and its role in continuing a delta sequence. The description adds partial value but leaves a required conceptual gap for proper invocation.

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?

Clearly states the action ('Execute a delta query') and the target resource, and gives concrete valid resource values. It does not explicitly distinguish itself from the sibling delta_messages tool, but the verb+resource pattern is specific enough to understand the core purpose.

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 on when to use this tool versus alternatives like delta_messages or the various list tools. The description implies there are supported resources but does not explain that this is for retrieving incremental changes over time, so an agent cannot infer when to choose it over a regular list or get operation.

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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