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

With no annotations, the description carries the full burden and does well: it discloses the corpus size, that transcripts are machine-generated, and adds a verification caveat before publishing quotes. It omits auth/access constraints and any rate or pagination behavior, keeping it out of 5 territory.

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 sentences, each earning its place: purpose, return shape, and a caveat. The corpus and use case are front-loaded before the accuracy warning, so nothing important is buried.

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 simple 3-parameter search tool with full schema coverage and no output schema, the description fills exactly the gaps that matter: what the search surfaces and the reliability caveat. An agent has everything needed to call it 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?

Schema description coverage is 100%, so the schema already documents query, show, and limit. The description adds no syntax, matching semantics (phrase vs. keyword), or filter behavior beyond that, so the baseline 3 applies.

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 and resource ('Full-text search across 320+ Onramp Media podcast transcripts'), names the corpus and example shows, and enumerates the returned fields. This is self-evidently distinct from the custody/ETF-centric siblings, so an agent can route to it without opening a schema.

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?

Gives explicit use cases ('what did X say about Y', sourcing a quote), which is clear contextual guidance. It stops short of naming an alternative or a when-not-to-use condition (e.g., vs. find_source for sourcing generally), so it is not the full 5.

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