research_tax_issue

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 a key behavioral trait: the tool follows a deterministic research plan and does not automatically finalize tax conclusions. However, it does not disclose what the output contains, whether it invokes other tools, whether it can fail, or any side effects. The disclosure is useful but incomplete for a research tool with no annotation support.

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 a single, compact sentence that front-loads the core purpose and includes a meaningful behavioral caveat. It is efficient and readable, though it could be slightly more structured by separating the caveat into its own sentence for clarity.

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?

The tool has 9 parameters, no annotations, and an output schema, but the description does not explain the research workflow, how parameters interact, or what the output represents. The 'does not auto-conclude' hint is valuable but leaves the agent uncertain about what the tool actually returns and how to interpret the result. For a complex research tool, this is a significant gap.

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 0%, so the description must compensate, but it does not explain any of the 9 parameters. The description mentions 'official sources' and 'tax conclusions' but does not clarify what 'issue', 'tax_type', 'jurisdiction', 'research_as_of', or 'upstream' mean semantically. The agent must infer parameter meaning entirely from names and defaults, which is risky for a complex research tool.

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 verb ('research') and resource ('tax issue'), and adds meaningful qualifiers: it collects and verifies official sources with a deterministic research plan, and explicitly does not auto-confirm tax conclusions. This distinguishes it from sibling tools like search_knowledge or get_applicable_law, though it doesn't name them explicitly.

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 implies this is the tool to use when a structured, evidence-based tax research process is needed, and the 'does not auto-conclude' phrase hints it is a research step rather than a final answer. However, it does not explicitly state when to use this tool versus siblings like search_legal_sources or get_research_report, nor does it mention prerequisites or exclusions.

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