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runwhen-contrib

RunWhen Platform MCP

workspace_chat

Investigate your infrastructure by asking the RunWhen AI assistant. It searches, correlates, and returns rich reports, diagrams, and tables.

Instructions

Ask the RunWhen AI assistant about your infrastructure.

This is the PRIMARY tool for investigating infrastructure. It sends your message to the RunWhen workspace AI agent, which searches (keyword, semantic, and fuzzy), navigates and correlates across resources, SLXs, issues, run sessions, and the knowledge base, and returns rich markdown reports, diagrams, and tables.

PREFER THIS TOOL over direct read/query tools (get_workspace_issues, get_workspace_slxs, search_workspace, etc.) for any question that involves searching by topic, keyword, or context — e.g. "issues related to neo4j", "what's failing in namespace X?", "health of the watcher cluster". workspace_chat produces materially better answers because it can search, filter, and correlate across all workspace data internally.

Use direct tools instead ONLY for: executing tasks (run_slx), task authoring, registry operations, chat config CRUD, KB mutations, or when you specifically need raw structured JSON for programmatic processing.

Returns: JSON with message, sessionId, widgets, chatUrl (full browser URL to continue this session in the RunWhen UI — run tasks, review history), and chatExportLink (shareable chat-export path when available).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesYour question or request about the workspace infrastructure.
session_idNoOptional session ID to continue a previous conversation.
persona_nameNoAI persona to use (default: 'default').default
workspace_nameYesThe workspace to query (e.g. 't-oncall').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Despite no annotations, description discloses behavior: sends message to AI agent, uses keyword/semantic/fuzzy search, correlates data, returns rich markdown reports. Also describes return fields (JSON with message, sessionId, etc.). Lacks explicit mention of side effects or rate limits, but overall thorough.

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?

Three well-structured paragraphs: purpose, functionality with usage guidance, and return format. Every sentence adds value; no redundancy.

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?

Given no annotations but presence of output schema, description adequately covers purpose, usage, behavior, and return values. Provides enough information for an agent to decide when and how to use this tool.

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 covers all 4 parameters with descriptions (coverage 100%). Description does not add additional parameter details beyond schema; however, it provides context on the tool's behavior and returns, which indirectly helps. Baseline applicable.

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?

Description clearly states tool asks the RunWhen AI assistant about infrastructure, positioning it as the primary investigation tool. It distinguishes itself from sibling tools by stating it produces better answers through cross-correlation.

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 advises preference over direct read/query tools and provides concrete examples (e.g., 'issues related to neo4j'). Also lists specific cases for using direct tools instead (executing tasks, task authoring, etc.).

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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