Federally tax-exempt municipal income

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

With no annotations, the description carries the transparency burden. It discloses the research nature and adds the 'not advice' disclaimer, plus the computed metrics. However, it doesn't describe the response format, update cadence, or any limitations. For a read-only data tool, this is adequate but not rich.

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 two sentences: the first is dense with specific metrics (tax-equivalent yields, brackets, distribution frequency, growth streak, years without a cut) and the second sets expectations ('Impersonal research data, not advice'). Every phrase earns its place, and it's well front-loaded with the subject.

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 simple zero-parameter nature and no output schema, the description is mostly complete: it explains what the tool returns and the key data points. It could specify the exact output format (e.g., a ranked table), but for a research list tool this is sufficient.

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 has zero parameters, so the baseline is 4 per the rubric. The description doesn't need to explain parameters and doesn't attempt to; it instead focuses on the returned data, which is appropriate.

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 identifies the tool's domain (tax-exempt municipal bond funds) and the outputs (ranked list with yields, frequency, etc.). It distinguishes itself from siblings like crypto_research and cheapest_fuel by topic. However, it lacks an explicit verb like 'list' or 'research,' making the action slightly implicit.

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 context is implied through the description: if the user needs municipal income fund data, this is the tool. But there is no explicit when-to-use guidance or mention of alternatives, and no exclusions or prerequisites are stated.

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