source_get_content

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

With no annotations, the description carries full burden. It explains the tool returns raw text from various source types (PDFs, web pages, etc.) and lists return fields. It lacks failure mode or prerequisite info, but is adequate for a simple read 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 three sentences plus Args/Returns structure, front-loaded with the core purpose. Every sentence adds value: core action, scope/comparison, and output specification. No wasted words.

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 tool with one parameter and an output schema listed in description, it covers purpose, parameter meaning, return fields, and usage context. Could mention that the source must exist or error handling, but overall complete enough.

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 schema has 0% description coverage, but the description adds 'Source UUID' for source_id, clarifying its purpose. It also lists return fields, which indirectly explains the parameter's role. This adds meaningful context beyond the bare schema.

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 the verb 'get' and resource 'raw text content of a source' while noting 'no AI processing', which distinguishes it from sibling tools like notebook_query that provide AI-processed content.

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 explicitly contrasts with notebook_query, stating it's 'much faster than notebook_query for content export', giving clear guidance on when to use this tool over an alternative. However, it does not list scenarios to avoid or mention other alternatives like source_describe.

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