Get company financials

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool parses iXBRL accounts, returns specific financial metrics for current and prior years, includes accounts type and data quality information, and mentions caching for 7 days. This covers data source, output structure, and performance characteristics, though it doesn't detail error handling or rate limits.

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 front-loaded with the core purpose, followed by specific details on returned data, accounts type, data quality, and caching. Every sentence adds value without redundancy, making it efficient and well-structured for an AI agent to understand the tool's scope and behavior.

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 tool with no output schema and no annotations, the description provides strong contextual completeness. It details the returned financial fields, accounts type, data quality block, and caching behavior. However, it doesn't specify the output format (e.g., JSON structure) or potential error scenarios, leaving some gaps for an agent to infer.

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 100% description coverage, with the company_number parameter well-documented in the schema itself (including pattern and example). The description adds no additional parameter semantics beyond what the schema provides, such as explaining how the company number relates to financial data retrieval. The baseline score of 3 is appropriate given the schema's comprehensive coverage.

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 tool's purpose with specific verbs ('Get structured financial data') and resources ('UK company'), distinguishing it from siblings like get_company (general info), get_directors (people data), get_network (relationships), and search_company (search functionality). It specifies the data source (iXBRL accounts from Companies House) and the type of financial data returned.

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 provides clear context for when to use this tool: to retrieve financial data for UK companies from Companies House filings. It implies usage by specifying the data source and company number parameter, but does not explicitly state when not to use it or name alternatives among siblings (e.g., use get_company for non-financial data).

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