Skip to main content
Glama

database_deploy_from_template

Deploy a pre-configured database using Railway's official templates and best practices for PostgreSQL, MongoDB, Redis, and other common database types.

Instructions

[WORKFLOW] Deploy a pre-configured database using Railway's official templates and best practices

⚡️ Best for: ✓ Standard database deployments ✓ Quick setup with security defaults ✓ Common database types (PostgreSQL, MongoDB, Redis)

⚠️ Not for: × Custom database versions × Complex configurations × Unsupported database types

→ Prerequisites: database_list_types

→ Alternatives: service_create_from_image

→ Next steps: variable_list, service_info

→ Related: volume_create, service_update

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesID of the project where the database will be deployed
typeYesType of database to deploy (e.g., postgresql, mongodb, redis). Use service_create_from_image for other types.
regionYesRegion where the database should be deployed, try us-west1 before all other regions
environmentIdYesEnvironment ID where the database will be deployed (usually obtained from project_info)
nameNoOptional custom name for the database service. Default: {type}-database
Behavior4/5

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 effectively communicates that this is a deployment tool for standard configurations with security defaults, implying it's a write operation. However, it doesn't explicitly mention permission requirements, rate limits, or what happens on failure, leaving some behavioral aspects unspecified.

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 (workflow context, best for, not for, prerequisites, alternatives, next steps, related tools) and uses bullet points and symbols efficiently. Every sentence earns its place by providing actionable guidance without unnecessary verbosity.

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 deployment tool with 5 parameters, 100% schema coverage, but no annotations or output schema, the description provides strong contextual completeness. It covers purpose, usage guidelines, prerequisites, alternatives, and next steps. The only gap is the lack of explicit behavioral details like error handling or response format, which would be more critical if the schema coverage were lower.

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 parameters thoroughly. The description doesn't add significant meaning beyond what's in the schema, though it contextualizes the tool as using 'pre-configured templates' which aligns with the enum options. The baseline of 3 is appropriate when the 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 ('Deploy a pre-configured database') and resource ('using Railway's official templates and best practices'), distinguishing it from sibling tools like service_create_from_image. It explicitly mentions the workflow context and standard database types, providing a precise purpose statement.

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, names a prerequisite (database_list_types), and specifies an alternative (service_create_from_image). It also suggests next steps and related tools, offering comprehensive usage context that helps differentiate when to use this tool versus others.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/RuKapSan/railway-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server