Azure DevOps MCP Server

get_pull_request_changes

Retrieve the files changed in a pull request, including unified diffs, branch names, and policy evaluation status.

Instructions

Get the files changed in a pull request, their unified diffs, source/target branch names, and the status of policy evaluations

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": {
      "repositoryId": "<repositoryId>",
      "pullRequestId": "<pullRequestId>"
    },
    "name": "get_pull_request_changes"
  }
}

Input Schema

Name	Required	Description
`projectId`	No	The ID or name of the project (Default: MyProject)
`repositoryId`	Yes	The ID or name of the repository
`pullRequestId`	Yes	The ID of the pull request
`organizationId`	No	The ID or name of the organization (Default: mycompany)

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.0.0Jan 2, 2026

TDQS

A3.7/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states the tool 'gets' data, implying a read operation, but does not explicitly confirm it is read-only, mention authorization needs, rate limits, or describe error behavior. This is insufficient for full transparency.

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 sentence that fully conveys the tool's purpose without any extraneous information. It is front-loaded with the core action and details the key outputs, earning its place with no waste.

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

Completeness3/5

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

Given four parameters and no output schema, the description lists the returned data (diffs, branches, policy status) but omits detail on output format, pagination, error handling, or large data scenarios. While helpful, it is not fully complete for an agent to handle all cases.

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 input schema already documents all four parameters. The description adds no further detail about parameter semantics beyond what the schema provides. Baseline score of 3 is appropriate as no extra value is added.

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 uses a specific verb 'Get' and clearly identifies the resource ('files changed in a pull request') and the specific data returned (unified diffs, branch names, policy evaluations). It effectively distinguishes this tool from siblings like get_pull_request, which likely returns metadata only.

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 implies when to use the tool (when needing diffs, branches, policy status) but does not explicitly state when not to use it or provide direct comparisons to alternatives. The context from the sibling list helps, but the description itself lacks explicit guidance.

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

Deploy Server