RunWhen Platform MCP

Get Workspace Slxs

get_workspace_slxs

Retrieve every SLX in a RunWhen workspace as structured JSON, auto-paginating all pages so you get the complete list for counting or batch operations.

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.

The underlying PAPI endpoint is paginated (100 SLXs per page); this tool auto-paginates internally, following next until exhausted, so the returned results are the complete list for the workspace — never just the first page. count reflects the true total and next is always null (there is nothing left to page).

It 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

Name	Required	Description	Default
`workspace_name`	Yes	The workspace to query (e.g. 't-oncall').

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 7, 2026

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations present, the description carries the full burden and does so well: it discloses that the underlying endpoint paginates at 100, that the tool auto-paginates to completion, that 'count' is the true total and 'next' is always null, and that unsupported keywords raise unexpected_keyword_argument.

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 and the 'no filtering' constraint, then well-structured bullets for alternatives. It is somewhat verbose on pagination internals, but each block serves a distinct decision the agent must make.

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?

An output schema exists so return values need not be explained, yet the description still clarifies the pagination semantics of the output. Combined with the alternative routing and parameter exclusions, an agent has everything needed to call this correctly.

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 schema already documents workspace_name and the baseline is 3. The description adds negative parameter semantics by enumerating what is NOT accepted (slx_name, filter, alias, tag), which meaningfully reduces mis-invocation risk.

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 states a specific verb and resource ('List ALL SLXs in a workspace'), names the return shape (structured JSON), and explicitly scopes out filtering. It also distinguishes itself from get_slx_runbook and workspace_chat, so an agent can select it without opening a sibling schema.

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 gives explicit routing for three shapes of need: one SLX -> get_slx_runbook, topic search -> workspace_chat, resource search -> workspace_chat. It closes with a clear when-to-use condition ('only when you need to enumerate every SLX for programmatic processing').

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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