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 important behavioral traits: data is computed daily, returns a summary by default with optional row-level data via include_rows, includes provenance (Power to Choose, EFL, EIA, BLS), and specifies licensing terms (free for non-commercial, Pro for commercial). It also notes data limitations (e.g., 'not verified by the FCC') and calculation methods. However, it doesn't describe response format, pagination, or rate limits, which are relevant for a data-heavy tool.

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 extremely long, with a single paragraph-like block listing all 11 slugs and their definitions. While informative, it is not front-loaded: the core purpose ('original data products about Texas electricity plans') is buried at the start, followed by a massive enumeration. It would benefit from better structure, such as a bulleted list or separate sections, and the license and citation info at the end feels tacked on. The length is disproportionate to the tool's complexity (2 parameters).

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 complexity—a multi-dataset tool with 11 distinct products—the description provides comprehensive details about each slug, including data sources, update frequency, and limitations. Since there is no output schema, the description adequately explains what each slug returns (e.g., 'returns the summary'). It also covers licensing and citation requirements. However, it could be more complete by clarifying whether the summary includes metadata or row counts, and by explicitly stating the return format (e.g., JSON structure).

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?

Schema description coverage is 50%; the slug parameter has an enum but no description in the schema, while include_rows has a description. The description enumerates all possible slug values with detailed explanations, effectively compensating for the missing schema description. However, it doesn't add syntax or format details beyond what the enum provides. For include_rows, the description says 'set include_rows=true for every plan row', which is slightly more contextual than the schema's 'Also return every plan row (large).'

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 what the tool does, which is to return original data products about Texas electricity plans computed daily. It distinguishes itself from siblings by enumerating 11 specific slugs, each with a detailed definition, making it clear this is a catalog of datasets rather than a computation tool. However, the purpose could be more succinctly stated at the top, and the distinction from other market_* tools is implied but not explicit.

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?

Usage guidance is implicit: by listing all slugs, the description tells the agent what data is available, but it does not explicitly state when to use this tool versus siblings like market_grid_demand or electricity_price_trends. The agent must infer that this is for accessing original, precomputed data products. The inclusion of 'Cite the page url' and license terms adds some operational context but not selection guidance.

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