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

With no annotations, the description carries the full burden and delivers significant value: server-side ticker pushdown, ticker-ignored fallback for internal-id-keyed datasets, required ticker for implied_vol_by_ticker, full history for cds_composites, and the unfiltered read cap. It does not cover auth/rate limits or response format, but the most critical behavioral traits are disclosed.

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?

Three dense sentences with zero filler; the main purpose is front-loaded and each subsequent clause adds a distinct, useful behavioral detail. The description is appropriately sized for the tool's complexity and does not waste words.

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 heterogeneous dataset reader with no output schema, the description covers the essential operational context: discovery path, filtering behavior, special-case slugs, and row cap. It omits output shape and explicit limit semantics, but the most likely causes of agent error are addressed, making it reasonably complete.

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 coverage is low (33%) and the dataset description is purely tautological ('Path parameter'), so the description compensates effectively: 'dataset' is explained as a slug, 'ticker' gets extensive behavioral semantics, and 'limit' is implied by the 5000-row cap. The limit parameter still lacks a one-line explanation, but its schema constraints (min/max/default) partially fill that gap.

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 opens with 'Read any licensed research dataset by slug' – a specific verb and resource that clearly defines the tool's function. It also distinguishes itself from the sibling discovery tool tengu_v2_research_datasets by explicitly referencing it for discovery, making the read-vs-discover boundary unmistakable.

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 concrete usage context: discover datasets via tengu_v2_research_datasets, use ?ticker= for filtering, and be aware of special slugs (implied_vol_by_ticker, cds_composites) and the 5000-row cap. It does not explicitly list alternative read tools, but the guidance is sufficient for an agent to know when this tool is appropriate.

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