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RunWhen Platform MCP

Create Assistant

create_assistant

Create a new AI assistant persona in a RunWhen workspace to tailor how the AI investigates and acts for a specific tech stack or team.

Instructions

Create a new AI assistant (persona) in a workspace.

Skill: runwhen-skill://create-ai-assistant (full setup workflow).

An assistant is a persona that tailors how the RunWhen AI investigates and acts — e.g. an "Azure DevOps Helper" focused on a specific tech stack. The short_name you choose becomes the persona_name for workspace_chat.

After creating the assistant, shape its behavior by attaching persona-scoped rules and commands:

create_chat_rule(scope_type="persona", scope_id=short_name, ...)
create_chat_command(scope_type="persona", scope_id=short_name, ...)

This is an UPSERT — calling it again with an existing short_name REPLACES the assistant's full configuration (omitted fields reset to defaults). To change a few fields on an existing assistant, use update_assistant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
avatar_urlNoOptional avatar image URL (e.g. '/personas/Man1-Happy.svg').
run_configNoRun configuration: allow/disallow/budget settings (advanced).
short_nameYesAssistant short name (lowercase-kebab-case, e.g. 'azure-devops'). Workspace prefix optional (e.g. 'my-ws--azure-devops'). This is the value you pass as persona_name to workspace_chat.
descriptionNoWhat this assistant specializes in (e.g. tech stack, team).
display_nameNoHuman-readable display name (e.g. 'Azure DevOps Helper').
filter_scopeNoOptional scope filter for results (advanced).
search_filtersNoVector-search filter operators (e.g. {'codebundleTaskTags': ['kubernetes'], 'slxGroup': ['my-group']}).
workspace_nameYesThe workspace to create the assistant in.
filter_stop_wordsNoWords stripped from search queries before matching.
run_confidence_thresholdNoConfidence threshold for automatic task runs (0-1).
filter_codebundle_task_tagsNoOnly surface tasks tagged with these (e.g. ['azure', 'devops']). Empty/omitted means no tag filter.
filter_confidence_thresholdNoConfidence threshold for filtering results (0-1).
filter_issue_selection_strategyNoIssue selection strategy (e.g. 'MOST_SEVERE').MOST_SEVERE

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does the important part: it flags this is an UPSERT where a repeat call with an existing short_name REPLACES the entire configuration and resets omitted fields to defaults — a critical destructive behavior that no structured field conveys. It omits permission/auth requirements and any failure modes, but the key mutation semantics are disclosed.

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?

Front-loaded with the core action, then procedurally organized (definition, follow-up calls, UPSERT warning). The persona explanation and skill pointer take space but each earns it; only the example persona sentence is slightly expendable.

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

Completeness5/5

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

For a 13-parameter creation tool with an output schema, the description covers the essentials an agent needs: what is being created, the identity linkage to workspace_chat, the setup follow-ups, and the dangerous upsert semantics. Return values are covered by the output schema, so nothing material is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description still adds meaning by linking short_name to the persona_name consumed by workspace_chat and distinguishing it from display_name. Advanced params like run_config, search_filters, and filter_* are left to the schema.

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 first sentence gives a specific verb+resource (create a new AI assistant/persona in a workspace) and then explains what an assistant actually is, so the agent knows the domain concept without opening the schema. It also explicitly differentiates itself from the sibling update_assistant.

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?

It names the alternative ('To change a few fields on an existing assistant, use update_assistant'), points at the full setup workflow skill, and describes the follow-up sequence (create_chat_rule / create_chat_command with scope_type='persona'). When to use, when not to, and what to do next are all explicit.

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