Skip to main content
Glama
runwhen-contrib

RunWhen Platform MCP

Search Registry

search_registry

Find reusable automation in the RunWhen CodeBundle Registry before writing custom scripts. Returns codebundles with tasks, SLIs, and deployment metadata.

Instructions

Search the RunWhen CodeBundle Registry for reusable automation.

Skill: runwhen-skill://find-and-deploy-codebundle (search → deploy workflow).

Use this BEFORE writing a custom script — there may already be a production-ready codebundle for the task. Returns codebundles with their tasks, SLIs, required env vars, and deployment metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoComma-separated support tags (e.g. 'GKE,KUBERNETES').
searchYesFree-text search (e.g. 'kubernetes pod health', 'postgres backup').
platformNoFilter by platform (e.g. 'Kubernetes', 'GCP', 'AWS').
max_resultsNoMax results to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the read-only search nature and the shape of results (tasks, SLIs, required env vars, deployment metadata), but adds nothing about authentication, result limits beyond the param, or rate behavior. Adequate, with clear gaps for a no-annotation tool.

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, followed by the routing hint and the value proposition. Every sentence earns its place; only the '(search → deploy workflow)' parenthetical is slightly compressed but still useful.

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?

With an output schema present, return values need not be explained, and the description covers purpose, timing, and follow-on workflow. It is complete enough for an agent to select and call it correctly, lacking only fallback-tool guidance.

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 description coverage is 100%, so the four parameters (search, tags, platform, max_results) are already fully documented with examples. The description adds no syntax or formatting guidance beyond the schema's 'comma-separated' tags note, so the baseline 3 applies.

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?

States a specific verb and resource ('Search the RunWhen CodeBundle Registry') and scopes it to 'reusable automation.' It is clearly distinguishable from siblings like get_registry_codebundle and deploy_registry_codebundle, which operate on a single already-identified codebundle rather than searching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly prescribes when to use it ('Use this BEFORE writing a custom script' and 'there may already be a production-ready codebundle'), and routes to the follow-on skill search → deploy workflow. It stops short of naming the concrete alternatives (run_script / validate_script) as fallbacks, so it is strong but not fully exhaustive.

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