get_data_sources

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

With no annotations provided, the description carries the full burden. It discloses what the tool returns (configuration, remaining quota, and limit-raising guidance) and the context of use. However, it does not explicitly state that the operation is read-only/non-mutating, though the wording strongly implies it.

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?

Two sentences, with the primary purpose front-loaded and the usage guidance following. No filler words; every phrase contributes meaning.

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?

Given the tool has no parameters and an output schema exists, the description fully covers the tool's purpose and when to use it. It doesn't need to explain return values because the output schema handles that, so no essential context is missing.

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?

There are zero parameters, so schema coverage is 100% vacuously. The description adds no parameter-specific detail, which is appropriate; the baseline for zero-param tools is 4, and no confusion is introduced.

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 states a specific resource ('configuration and remaining quota for every external data source') and includes an additional deliverable ('how to raise the limits'). This clearly distinguishes it from sibling tools that fetch specific data types (e.g., news, OHLCV) by focusing on data source health and limits.

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?

Explicitly tells the agent when to use this tool: 'Use this when a data tool fails or seems rate-limited.' This is a concrete trigger that directly supports tool selection, and it implies other tools are for actual data retrieval rather than quota diagnostics.

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