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runwhen-contrib

RunWhen Platform MCP

update_assistant

Update specific settings of an existing AI assistant without resetting other configurations.

Instructions

Partially update an existing AI assistant (persona).

Fetches the current configuration, applies only the fields you provide (leaving everything else intact), then writes the merged result back. Use this instead of create_assistant when you only want to change a few settings without resetting the rest.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
avatar_urlNoNew avatar image URL.
run_configNoReplace the run configuration.
descriptionNoNew description.
display_nameNoNew display name.
filter_scopeNoNew scope filter.
assistant_nameYesAssistant short name to update (e.g. 'azure-devops'). Workspace prefix optional.
search_filtersNoReplace the vector-search filters.
workspace_nameYesThe workspace the assistant belongs to.
filter_stop_wordsNoReplace the stop-words list.
run_confidence_thresholdNoNew run confidence threshold (0-1).
filter_codebundle_task_tagsNoReplace the task-tag filter list.
filter_confidence_thresholdNoNew filter confidence threshold (0-1).
filter_issue_selection_strategyNoNew issue selection strategy.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Describes the partial update behavior (fetch, merge, write) but lacks details on permissions, error handling, or side effects. With no annotations, the description carries the full burden and is adequate but not comprehensive.

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?

Two concise sentences: first states purpose, second explains mechanism and suggests alternative. No redundant information, front-loaded.

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

Completeness4/5

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

Covers key aspects: purpose, partial update mechanism, and when to use vs create. Has output schema so return values are covered. Sibling tools are many, but differentiation from create_assistant is sufficient.

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?

All parameters are described in the input schema (100% coverage). The description adds general context about partial updates but does not provide per-parameter meaning beyond schema descriptions.

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 'Partially update an existing AI assistant (persona)' with a specific verb and resource. It distinguishes from the sibling tool 'create_assistant' by explaining when to use this tool instead.

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?

Explicitly states 'Use this instead of create_assistant when you only want to change a few settings without resetting the rest,' providing clear when-to-use and alternative context.

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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