read_dag

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

No annotations are supplied, so the description carries full responsibility for behavioral disclosure. It explicitly lists accepted inputs (path/content), returned fields (source, file_path, file_size_bytes, line_count), and an error possibility. While it doesn't mention potential edge cases (e.g., file-not-found, mutual exclusivity of params), it is transparent for a read-only operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and logically structured: a clear purpose statement, a brief note on accepted input forms, then Args and Returns sections. Every sentence earns its place; no filler or redundancy.

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

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

For a simple read tool with an output schema, the description is complete: it states what it does, what inputs it accepts, what it returns, and that it can error. The output schema covers the return structure, and the description enumerates the key fields. No important information is missing given the tool's straightforward nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameter descriptions (0% coverage), so the description's Args section is essential. It defines path as 'Path to a DAG file on disk' and content as 'Inline DAG source code', adding meaning beyond the bare parameter names. It stops short of specifying mutual exclusivity or expected content format, but it provides adequate semantic grounding.

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

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

The description clearly states 'Read an Airflow DAG file and return raw source with metadata' – a specific verb, resource, and expected output. This distinguishes it from sibling tools like lookup_concept or generate_deployment, which serve different purposes.

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

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

The description clearly implies when to use this tool (whenever you need to read a DAG file's source or inline code) but lacks explicit alternatives or exclusions. Sibling tools are conceptually different, so confusion is unlikely, but no direct 'use this instead of X' guidance is provided.

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