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Get app logs

get_app_logs

Fetch application and deployment logs from mapped providers for a project environment. Optionally filter by provider, time range, or specific deployment.

Instructions

Fetch application/deployment logs for a project environment from the mapped provider(s). If provider is given, reads that provider only; otherwise reads every mapped provider that supports logs (Vercel + Railway in V0, Vercel prioritized). Returns the resource used, time range, log lines, and any API limitation. Reads are allowed everywhere and are audited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax log lines (default 100)
sinceNoOnly logs after this time (epoch ms or ISO timestamp)
projectNoProject id or slug; uses selected if omitted
providerNoRestrict to one provider (e.g. 'vercel')
environmentYesEnvironment id or name
deployment_idNoSpecific deployment to read logs for
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that reads are allowed everywhere and audited, and mentions API limitations. However, it doesn't address potential rate limiting, error cases, or the format of the 'API limitation' field, leaving some gaps.

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 two sentences with no wasted words. It front-loads the core action and then provides essential details in a structured manner, making it easy for an AI agent to parse quickly.

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 6 parameters and no output schema, the description covers the return fields (resource, time range, log lines, API limitation) and default provider behavior. It lacks details on error handling or pagination, but is largely complete for a log-fetching tool.

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 coverage is 100%, so baseline is 3. The description adds context about the default provider behavior (Vercel prioritized) and the effect of omitting the 'project' parameter, but does not significantly expand on the schema descriptions.

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 tool fetches application/deployment logs for a project environment, specifying the behavior for single vs multiple providers and listing return fields. It also distinguishes itself from sibling tools like 'get_latest_deployment_logs' by detailing the provider selection logic.

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

Usage Guidelines4/5

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

The description explains when to use the provider parameter versus when to read all mapped providers, and mentions that reads are audited. It could be more explicit about when to prefer this tool over 'get_latest_deployment_logs', but the context is sufficient for an AI agent.

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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