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mcp-server-github-actions

trigger_workflow

Manually start GitHub Actions workflow runs on specified branches with custom inputs for CI/CD automation.

Instructions

Trigger a workflow run via workflow_dispatch. Requires a workflow with workflow_dispatch enabled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ownerYesRepository owner (username or org)
repoYesRepository name
workflow_idYesWorkflow ID or filename (e.g. ci.yml)
refNoBranch or tag to run on (default: main)main
inputsNoOptional workflow inputs as key-value pairs
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the requirement for 'workflow_dispatch enabled' but fails to describe key traits such as whether this is a write operation (implied by 'trigger'), potential side effects, authentication needs, rate limits, or what happens upon execution. This leaves significant gaps in understanding the tool's behavior.

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?

The description is concise with two sentences that directly state the purpose and a key requirement. It is front-loaded with the main action and avoids unnecessary elaboration. While efficient, it could be slightly improved by integrating the requirement more seamlessly, but overall it earns its place without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of triggering a workflow (a write operation with potential side effects), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, error handling, return values, and how it differs from sibling tools. This makes it inadequate for safe and effective use by an AI agent in a real-world context.

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%, meaning all parameters are documented in the schema itself. The description adds no additional parameter details beyond what the schema provides, such as examples or constraints. However, since the schema is comprehensive, a baseline score of 3 is appropriate as the description doesn't need to compensate for gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('trigger a workflow run') and mechanism ('via workflow_dispatch'), which is specific and actionable. It distinguishes from siblings like 'list_workflows' or 'get_run' by focusing on execution rather than querying. However, it doesn't explicitly differentiate from 'rerun_workflow' or 'rerun_failed_jobs', which are also execution-related siblings.

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

Usage Guidelines3/5

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

The description provides some context by mentioning the prerequisite ('Requires a workflow with workflow_dispatch enabled'), which helps determine when it can be used. However, it offers no guidance on when to use this tool versus alternatives like 'rerun_workflow' or 'rerun_failed_jobs', nor does it specify exclusions or complementary tools, leaving usage decisions ambiguous.

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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