DataClue — AI training data

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

Annotations already state readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds useful return-scope detail by listing the dataset fields exposed, though it does not mention missing-slug behavior, auth requirements, or rate limits.

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 front-loaded sentence with no filler, and the return-field list is relevant. The long comma-separated enumeration is dense but still efficient.

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-only lookup with no output schema, the description sufficiently explains what is returned. Annotations cover safety traits, though the description could ideally mention behavior when the slug is unknown.

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% and the slug parameter includes concrete examples, so the schema does the heavy lifting. The description adds no syntax or format guidance beyond what the schema provides, making the baseline 3 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 names a specific retrieval operation and resource: full details for one DataClue dataset. It clearly distinguishes a single-dataset lookup from a broad search, though it does not explicitly name siblings like search_datasets.

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 is only implied by “one DataClue dataset” and the required slug parameter, suggesting it is for when the slug is already known. It gives no explicit when-to-use guidance or comparison with search_datasets, get_pricing_and_terms, or request_quote.

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 readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the safety profile needs no restating. The description still adds value by scoping the exact content covered, though it says nothing about freshness, sourcing, or rate limits.

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?

A single dense sentence with no filler, and the core content (prices) is front-loaded. It reads as a noun list rather than a verb-led statement of purpose, which slightly weakens the structure.

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?

With no output schema, the description carries the burden of describing return content, and its enumeration of price, inclusions, quote paths, licensing and payment terms does that well. Missing only framing details like return format or how current the data is.

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?

Zero parameters, so per the baseline this scores 4. There is nothing parameter-related for the description to compensate for.

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 names the resource precisely (list prices, inclusions, quote paths, licensing, payment and consent terms) so an agent knows exactly what content it returns. It doesn't explicitly contrast itself with siblings like request_quote, so the distinction is inferable rather than stated.

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?

Listing 'quote paths (free samples, paid pilot, volume quote, custom collection)' implies this is the informational lookup that precedes the action tool request_quote, but no explicit when-to-use or when-not-to-use guidance is given. The agent must infer that request_quote is the sibling to call for the actual quote.

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 flag destructiveHint and openWorldHint, but the description adds the concrete reason: it emails the user's name, work email and company to an external party, and the reply comes back to that address. It also discloses the non-idempotent consequence in practical terms ('each call sends a new request, so do not retry'), which annotations alone cannot convey.

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 tight sentences: what it does, what the side effect is, and the guardrails for calling it. The side-effect disclosure and the confirmation requirement are front-loaded where an agent will see them before acting.

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 10-parameter outbound-emailing tool with no output schema, the description covers the critical unknowns: external side effect, PII transmitted, reply channel, and no-retry semantics. It does not describe failure behavior or validation errors, but nothing essential to a correct call is missing.

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 every parameter's meaning, enum values, and conditional requirements (hours for volume, details for custom, datasets for pilot/volume) are already documented in the schema. The description only restates the PII fields being emailed and adds no syntax or format guidance beyond that, so 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 ('Send a request to DataClue's sales team') and enumerates the four request types (samples, pilot, volume quote, custom collection). This clearly separates it from read-only siblings like get_pricing_and_terms or search_datasets.

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 an explicit precondition ('Only call this when the user has asked to contact DataClue and has provided their own work email'), a confirmation step ('show them the details to confirm before sending'), and a retry rule ('do not retry a successful call'). Both when-to-use and when-not-to-act are spelled out.

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 establish that this is a safe, read-only, closed-world search. The description adds useful behavioral context beyond annotations: it says results include hours in stock, price per hour, scenario fit, requirement coverage, and custom options when stock does not fit. Pagination or result limits are not mentioned, keeping it from a 5.

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?

Two sentences, front-loaded with the search action and catalog scope, then the example and return-value summary. Every element earns its place without repetition.

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 one-parameter search tool with annotations covering safety and no output schema, the description supplies enough context: what to query, what the response contains, and what happens when nothing matches. An agent has what it needs to invoke 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?

With one parameter and 100% schema description coverage, the schema already documents the query dimensions. The description adds meaning by emphasizing that the query should be a plain-English need and gives a concrete example, which helps an agent formulate the input correctly.

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 ("Search") and resource ("datasets") and scopes it to a licensable data catalog with concrete examples. It is clearly distinguishable from sibling tools like get_dataset or request_quote, which imply detail retrieval and quoting rather than discovery search.

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 clearly indicates when to use the tool: when you have a plain-English data need and want matching inventory plus custom-collection fallback. It does not explicitly name when to move to siblings such as get_dataset or get_pricing_and_terms, so it falls short of full when/when-not routing.

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