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ado_update

Update an existing Azure DevOps work item by ID, modifying titles, states, priorities, assignees, tags, and custom fields.

Instructions

Update an existing work item in Azure DevOps

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNoNew area path
tagsNoSemicolon-separated tags
stateNoNew state
titleNoNew title
fieldsNoAdditional ADO fields as key-value pairs
reasonNoReason (e.g. for closing)
priorityNoPriority (1=highest)
iterationNoNew iteration path
assigned_toNoNew assignee email or display name
descriptionNoNew description (HTML)
story_pointsNoStory points
work_item_idYesWork item ID to update
acceptance_criteriaNoNew acceptance criteria (HTML)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and 'Update an existing work item' only signals mutation. It does not explain whether the update merges only supplied fields or replaces the item, whether state transitions (e.g., closing) require a reason, whether permissions are needed, or what the tool returns. For a high-impact mutation tool this is a significant gap.

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?

A single 7-word sentence with no filler; the verb and object are front-loaded and every word earns its place. It reads as genuine conciseness rather than under-specification because the sentence conveys real meaning, though it is on the minimal side for a 13-parameter tool.

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 mutation tool with 13 parameters, no output schema, and no annotations, the description is too thin. It omits key context an agent needs: merge-vs-replace semantics, behavior on missing/empty fields, return value, and how it differs from ado_create, ado_fetch, and ado_show. The schema covers parameters, but behavioral context is entirely absent.

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% — all 13 parameters have descriptions in the schema — so the baseline is 3. The description adds no parameter-level detail, but it does not need to because the schema fully documents each field, including the nested 'fields' object.

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 ('Update') and resource ('an existing work item in Azure DevOps'). The qualifier 'existing' implicitly separates it from create/read siblings like ado_create, ado_fetch, and ado_show, but it never names a sibling or an explicit contrast, so differentiation is implicit rather than stated.

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 its 11 siblings. The phrase 'existing work item' implies a modification scenario, but there are no explicit conditions, exclusions, or pointers to alternatives such as ado_create for new items or ado_fetch/ado_show for reads. This mirrors the update_drive calibration case, which scored 2 for the same gap.

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