Mozilla Data Collective

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

Annotations already declare read-only, idempotent, and non-destructive behavior; the description adds that the data is 'public' and enumerates which fields will be returned. This extends the annotation context without contradicting it.

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 entire description is one dense sentence: the operation, input key, and expected return fields are all front-loaded with no filler or repetition of the title.

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 one-parameter read operation with full annotations, the description covers what is fetched and what is returned well enough. It omits explicit not-found or error behavior, but the absence of an output schema and low complexity keep this a minor 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 coverage is 100% and the parameter description already states it is a dataset id or slug from search results. The tool description adds no new semantic detail for the parameter beyond what the schema provides.

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?

Description names a specific verb ('Fetch'), a bounded resource ('one Mozilla Data Collective dataset'), and the lookup key ('by id or slug'), then enumerates the returned details. This is distinguishable from siblings 'search' and 'list_filters' without needing their schemas.

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 makes clear this is for retrieving full details for a single known dataset, which implies a post-search or post-list step. It does not explicitly name when to prefer 'search' or 'list_filters', so it falls short of full exclusion guidance.

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

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

Annotations already declare this as read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds useful behavioral context: value types are abbreviations, labels are provided to spell them out, and matches are exact. It stops short of describing the output structure, but for a no-argument listing tool this is sufficient.

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, front-loaded with the core purpose, and every sentence adds value: what is listed, the abbreviation nuance, and when to call it. There is no filler or repetition of schema information.

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 no-argument, read-only enumeration tool, the description is complete. It tells the agent what values will be returned, highlights the label/abbreviation distinction, and explains the appropriate usage timing. No output schema exists, but the description sufficiently conveys the content and purpose of the output.

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 and the schema already reflects that with an empty properties object. The description redundantly but helpfully states 'Takes no arguments,' leaving no ambiguity. Since there are no parameters to document, this dimension is fully satisfied.

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 uses a specific verb-resource pairing: 'List every value the search tool's filters accept' and enumerates the exact categories (tasks, locales, licenses, formats, sort, date-range). This clearly distinguishes list_filters from the sibling tools fetch and search, which retrieve data rather than enumerate filter options.

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?

It explicitly tells the agent when to use the tool: 'call this before filtering a search rather than guessing values.' It also warns that filter values are matched exactly, giving a concrete reason to use the tool rather than rely on assumptions. The instruction is direct and actionable.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral context beyond those: the exact return shape ({id, title, url}) and the exact-match requirement for filter values, which is important operational detail for the agent.

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: what the tool does, what it returns and how to continue with fetch, and the critical precondition for filtering. The information is front-loaded and tightly written.

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?

Despite 11 parameters, the schema fully covers them, and the description covers the essential non-schema context: return format, follow-up tool routing, and exact filter matching. With no output schema present, the explicit return shape fills the gap. Nothing critical is missing for an agent to invoke this 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 all 11 parameters thoroughly. The description's filter list adds a useful high-level summary but does not meaningfully enhance individual parameter semantics beyond what the schema already provides.

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 states a specific action ('Search'), a specific resource ('Mozilla Data Collective catalog of AI training datasets'), and the natural-language query mechanism. It also distinguishes itself from siblings by noting that matching dataset ids go to fetch and that list_filters should be called for filter values.

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 gives explicit when-to-use guidance: use natural-language search to find datasets, call list_filters before filtering, and pass an id to fetch for full details. This effectively routes the agent between search, list_filters, and fetch without ambiguity.

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