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runwhen-contrib

RunWhen Platform MCP

get_run_sessions

Retrieve recent run sessions for a workspace in structured JSON format. Use for programmatic access to raw execution data of health checks, troubleshooting tasks, and automation runs.

Instructions

Get recent run sessions for a workspace (structured JSON).

Run sessions are executions of SLX runbooks — they contain the output of health checks, troubleshooting tasks, and automation runs.

NOTE: For investigative questions like "what ran recently for service X?" or "show me recent failures", prefer workspace_chat — it can search, filter, and correlate run sessions with issues and resources. Use this tool only when you need raw JSON for programmatic processing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax run sessions to return.
workspace_nameYesThe workspace to query (e.g. 't-oncall').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided. Description mentions 'recent' but does not specify cutoff or sorting order, nor error handling (e.g., missing workspace). However, the presence of an output schema (context signal) reduces burden. Description adds some context about run sessions but lacks full behavioral disclosure.

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?

Description is concise: one sentence for purpose, one for context, and a clear usage note. No unnecessary words, front-loaded with core action.

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?

Given the tool's simplicity (2 params, no nested objects, output schema present), the description adequately explains purpose, usage, and output format. Minor gaps like limit behavior and sorting are omitted, but overall 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?

Schema coverage is 100% with parameter descriptions. Description adds general context ('workspace', 'recent') but does not provide additional semantics beyond the schema. Baseline score of 3 is appropriate.

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 the tool retrieves recent run sessions for a workspace in structured JSON. It explains what run sessions are and distinguishes from sibling tool workspace_chat by specifying when to use each.

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?

Explicit note advises preferring workspace_chat for investigative questions (searching, filtering, correlating) and using this tool only when raw JSON is needed for programmatic processing, providing clear when-to-use and when-not-to-use guidance.

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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