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service_create_from_repo

Deploy applications directly from GitHub repositories by creating services with build processes, ideal for source code projects requiring automated deployment.

Instructions

[API] Create a new service from a GitHub repository

⚡️ Best for: ✓ Deploying applications from source code ✓ Services that need build processes ✓ GitHub-hosted projects

⚠️ Not for: × Pre-built Docker images (use service_create_from_image) × Database deployments (use database_deploy) × Static file hosting

→ Prerequisites: project_list

→ Alternatives: service_create_from_image, database_deploy

→ Next steps: variable_set, service_update

→ Related: deployment_trigger, service_info

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesID of the project to create the service in
repoYesGitHub repository URL or name (e.g., 'owner/repo')
nameNoOptional custom name for the service
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions prerequisites and related tools, adding useful context about workflow dependencies. However, it doesn't describe mutation behavior (e.g., whether creation is reversible, permissions needed, or rate limits), which is a gap for a write operation tool.

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?

The description is well-structured with clear sections (purpose, best for, not for, prerequisites, alternatives, next steps, related). Every sentence adds value, and information is front-loaded with the core purpose stated first. No wasted words or redundancy.

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?

For a creation tool with no annotations and no output schema, the description does well by covering usage context, alternatives, and workflow dependencies. However, it lacks details on mutation behavior (e.g., what happens on failure, response format) which would be helpful given the absence of structured safety hints.

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 schema already documents all three parameters. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., format examples for 'repo' are in the schema). Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('Create a new service'), resource ('from a GitHub repository'), and scope ('[API]'). It explicitly distinguishes from sibling tools like service_create_from_image and database_deploy, making the purpose unambiguous and differentiated.

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?

The description provides explicit guidance with 'Best for' and 'Not for' sections, naming specific alternatives (service_create_from_image, database_deploy). It also lists prerequisites (project_list) and related tools, offering comprehensive usage context beyond basic functionality.

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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