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runwhen-contrib

RunWhen Platform MCP

get_workspace_slxs

Retrieve all SLXs (health checks and automation runbooks) for a workspace in structured JSON format. Use for counting or batch operations without filtering.

Instructions

List ALL SLXs in a workspace (structured JSON). No filtering.

SLXs are the fundamental unit of work in RunWhen — each represents a health check, task, or automation runbook for a piece of infrastructure.

This tool returns the full list for the workspace and accepts only workspace_name. It does NOT accept slx_name, filter, alias, tag, or any other filtering parameter — those would fail with unexpected_keyword_argument.

For other shapes:

  • One specific SLX (runbook detail): get_slx_runbook(workspace_name=..., slx_name=...)

  • Search / filter by topic (e.g. "neo4j health checks"): workspace_chat

  • Search by resource (e.g. "what monitors namespace X"): workspace_chat

Use this raw-list tool only when you need to enumerate every SLX for programmatic processing (counting, batch operations, etc).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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?

No annotations provided, so description carries full burden. It discloses that the tool returns full list, accepts only workspace_name, and warns that extra params fail. However, it doesn't mention pagination or rate limits, though the output schema exists to detail return structure.

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?

Well-structured with clear sections and examples, but could be slightly more concise. Still, every sentence adds value.

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 the single parameter and existence of output schema, the description fully covers purpose, constraints, and usage context. No gaps.

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

Parameters5/5

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

Schema coverage is 100%, and description adds crucial context: it clarifies that only workspace_name is accepted and explicitly lists rejected parameters, preventing misuse.

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?

Clearly states 'List ALL SLXs in a workspace' and distinguishes from siblings like get_slx_runbook and workspace_chat by naming them and their specific uses.

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 tells when to use this tool (programmatic enumeration) and when not to (use alternatives for specific SLX or filtered search). Also lists what parameters are rejected.

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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