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Azure DevOps MCP Server

Azure DevOps MCP Server

by allanbrunobr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

get_work_items_by_ids

Fetch multiple Azure DevOps work items by their IDs in a single request to improve efficiency compared to individual calls.

Instructions

Fetch multiple work items by their IDs in a single request. More efficient than calling get_work_item repeatedly.

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": {
      "ids": "<ids>"
    },
    "name": "get_work_items_by_ids"
  }
}

Input Schema

NameRequiredDescriptionDefault
idsYesArray of work item IDs (max 200)
fieldsNoOptional list of fields to return
expandNoExpand relations or fields

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions efficiency but doesn't disclose other behavioral traits like rate limits, authentication requirements, error handling, or response format. While it adds some context about batch operations, significant behavioral details remain undocumented.

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 perfectly concise with two sentences that each earn their place: the first states the core purpose, the second provides crucial comparative guidance. It's front-loaded with the main functionality and wastes no words.

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?

For a read operation with no annotations and no output schema, the description provides adequate purpose and usage guidance but lacks behavioral context about response format, error conditions, or system constraints. It's complete enough for basic understanding but leaves gaps that could affect agent performance.

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 all parameters thoroughly. The description doesn't add any parameter-specific meaning beyond what's in the schema (e.g., it doesn't explain ID formats, field options, or expand implications). Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Fetch multiple work items by their IDs') and resource ('work items'), distinguishing it from sibling tools like 'get_work_item' (singular) and 'list_work_items' (general listing). It explicitly mentions efficiency compared to repeated calls, which adds valuable differentiation.

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 provides explicit guidance on when to use this tool ('more efficient than calling get_work_item repeatedly'), directly comparing it to an alternative sibling tool. This gives clear context for choosing between batch fetching and individual retrieval.

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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