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deployment_logs

Retrieve logs for a specific deployment to debug issues, monitor progress, or check build output on Railway.app infrastructure.

Instructions

[API] Get logs for a specific deployment

⚡️ Best for: ✓ Debugging deployment issues ✓ Monitoring deployment progress ✓ Checking build output

⚠️ Not for: × Service runtime logs × Database logs

→ Prerequisites: deployment_list

→ Next steps: deployment_status

→ Related: service_info, deployment_trigger

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deploymentIdYesID of the deployment to get logs for
limitNoMaximum number of log entries to fetch
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It effectively discloses key behavioral traits: it's a read operation ('Get logs'), used for debugging/monitoring, with specific exclusions (not for service/database logs). However, it doesn't mention potential limitations like log format, pagination beyond the limit parameter, or error conditions, leaving some behavioral aspects uncovered.

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 well-structured with bullet points and arrows for readability, and every section (purpose, guidelines, prerequisites) adds value. It could be slightly more concise by combining some elements, but it's efficiently organized without wasted sentences.

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?

Given no annotations and no output schema, the description does a good job covering purpose, usage, and exclusions. It mentions prerequisites and related tools, which helps contextualize the tool. However, it lacks details on return values (e.g., log format, structure) and error handling, leaving some gaps for a tool with 2 parameters and no output schema.

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 fully documents both parameters (deploymentId and limit). The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints on limit values. Baseline 3 is appropriate when the schema does all the work.

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 ('Get logs') and resource ('for a specific deployment'), distinguishing it from siblings like deployment_status (which provides status, not logs) or service_info (which provides service details, not deployment logs). The title is null, but the description fully compensates with precise verb+resource specification.

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' (debugging, monitoring, checking build output) and 'Not for' (service runtime logs, database logs), plus clear prerequisites (deployment_list), next steps (deployment_status), and related tools (service_info, deployment_trigger). This comprehensively tells the agent when to use this tool versus alternatives.

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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