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Deploy Repository Tool

deploy-repository-tool

Trigger a production deploy for one of your team's connected repositories. The repository must have a deploy trigger configured — either a deploy hook URL or a GitHub Actions workflow — in its Coderbuds settings. Identify the repository by name (e.g. "saas") or by full name (e.g. "patchstack/saas"). This ships whatever is currently merged; it is not a dry run. Use GetUndeployedPullRequestsTool first if you want to see what will go out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repositoryYesThe repository to deploy, by name (e.g. "saas") or full name (e.g. "patchstack/saas"). Must be connected to your team and have a deploy trigger configured.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
methodYesHow the deploy was triggered: "hook" or "workflow".
messageYesA human-readable summary of the outcome.
successYesWhether the deploy was triggered.
repositoryYesThe repository that was deployed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

With empty annotations, the description carries the full burden and it does: this 'ships whatever is currently merged', is 'not a dry run', and requires a preconfigured deploy trigger. Those are exactly the mutation-effect and precondition facts an agent needs before invoking a write operation.

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?

Four sentences, each earning its place: action, precondition, identifier format, destructive nature, alternative tool. The most consequential fact ('not a dry run') is present and front-loaded enough to catch the eye.

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?

For a one-parameter mutation tool with an output schema already defining return values, the description covers action, precondition, identifier forms, irreversibility, and the read-first alternative. Nothing an agent needs to call it correctly is missing.

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 baseline is 3, but the description adds practical identification guidance — a short name like "saas" versus full name like "patchstack/saas" — plus the team-connection and deploy-trigger requirement. It goes slightly beyond the schema text without adding format details the schema already supplies.

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?

Specific verb ('Trigger a production deploy') and resource ('one of your team's connected repositories'), with the scope narrowed to production. An agent can distinguish it from read-only siblings like get-deployments-by-person-tool or get-undeployed-pull-requests-tool at a glance.

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 states a precondition (must have a deploy hook URL or GitHub Actions workflow configured), names the sibling to use first (GetUndeployedPullRequestsTool) and gives the condition that selects it ('if you want to see what will go out'). Nothing is left to inference.

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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