tally_list_companies

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

Annotations cover read-only and closed-world, but the description goes further: it explains that only currently-loaded companies are returned (a real operational constraint that could mislead an analyst), notes pagination is N/A because the list is small, and warns that text fields are data not instructions.

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?

Labeled sections (WHEN TO USE, RETURNS, DOES NOT RETURN, PAGINATION, safety note) front-load the purpose and let an agent skip to the relevant part. No sentence is redundant.

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?

With no output schema, the description compensates by defining the return shape (company name plus ISO YYYY-MM-DD book start date). Combined with the negative-scope note, an agent has everything needed to call and interpret this tool correctly.

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 tool takes zero parameters, so the baseline of 4 applies. The description correctly conveys there is nothing to configure, and the schema confirms the empty object shape.

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?

States a specific verb+resource+scope: list companies currently loaded in TallyPrime, including each set of books' start date. The negative scope ('companies that exist on disk but are not open') also implicitly distinguishes it from sibling tools like tally_get_company that query broader datasets.

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?

An explicit WHEN TO USE section names two concrete situations: pre-analysis confirmation of which company data will be used, and spelling verification for another tool. This tells the agent both when to reach for it and how it feeds downstream calls, leaving little to inference.

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