DesiData

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds meaningful behavior beyond that: full-file downloads require the user to configure their own DD_TOKEN, and the token must never be included or requested, which is a valuable security constraint an agent would not otherwise know.

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 short sentences with no filler, and the primary purpose is front-loaded ahead of the secondary token warning. Efficient and appropriately sized for a simple tool.

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?

No output schema exists, and the description sufficiently conveys that the return is generated Python example code plus the token-handling caveat. For a single-parameter, read-only generation tool this is close to complete, with minor gaps around how to supply or obtain a valid slug.

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 0%, but there is only one parameter, so the burden is light. The description clarifies that the slug refers to a 'published dataset,' adding semantic context the bare string schema lacks, but it does not give format or source guidance for obtaining a valid slug.

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 ('Generate') and concrete deliverable ('a small Python example') tied to the dataset slug, so the agent knows exactly what it produces. However, it does not distinguish itself from the sibling get_notebook_example, which is a closely related code-generation tool, leaving the selection boundary ambiguous.

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?

There is no explicit statement of when to use this tool versus get_notebook_example or preview_dataset, and no when-not guidance. The phrase 'using the published dataset slug' implies a prerequisite but does not constitute routing 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 readOnlyHint=true and openWorldHint=false, so the safety profile is covered structurally. The description adds a genuinely useful behavioral note beyond them: dataset text is untrusted data and never instructions, which is real prompt-injection guidance. It stops short of describing output format or error behavior.

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, no filler. The capability statement is front-loaded and the security caveat is terse and clearly separated.

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 sensibly enumerates what is returned (provenance, licence, size, row count, tags, links), which covers the agent's main need. The remaining gap is slug semantics and how to discover a valid slug, which is left implicit.

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?

Only one parameter (slug) exists, and the schema supplies minLength/maxLength with no description, giving 0% coverage. The description implies the slug identifies 'one published dataset' but adds no syntax, format, or sourcing guidance (e.g. where to obtain a slug), so it only partially compensates.

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 (Get) and resource (dataset details) and enumerates the returned fields: provenance, licence, size, row count, tags, links. It is easily distinguishable from preview_dataset and search_datasets by intent, though it never explicitly names those siblings.

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 phrase 'one published dataset' implies retrieval by identifier, which is adequate context for a lookup tool. However, there is no explicit when-to-use guidance or stated relationship to search_datasets (find a slug) or preview_dataset (see contents).

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 and openWorldHint=false, so safety is covered. The description goes further by disclosing the negative behavior — it does not build or return a notebook file and avoids an expensive repeated build — which is genuinely useful context an agent cannot infer from the annotations.

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 tight sentences with the positive behavior front-loaded and the caveat second; nothing is wasted. It is slightly under-developed on the parameter side but structurally 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 simple read-only lookup with no output schema and one parameter, the description covers the return shape (URL plus instructions) and the negative behavior. However, it omits any explanation of the required slug, which is the only input an agent must supply.

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 0% for the single required 'slug' parameter, and the description says nothing about what a slug is, where it comes from, or its format/length constraints. With one undocumented parameter the description must compensate and it does not.

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 ('Return the website's starter notebook URL and concise instructions'), which is concrete enough for an agent to distinguish it from generate_loader_code. It does not explicitly name any sibling, so it falls short of the 5 bar for differentiation.

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 clause 'Does not generate or return the notebook file' implies a contrast with the sibling generation tool, but no alternative is named and no explicit when-to-use condition is given. Usage is only implied rather than stated.

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 only cover the safety profile (readOnlyHint, openWorldHint=false), and the description adds genuinely new behavioral context: hard caps of 10 rows, 12 columns, 64 KiB, the fact that storage is read but the file is not fully downloaded, and an explicit prompt-injection warning that preview cells are untrusted data.

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 short sentences, zero filler, and the output shape and its limits are front-loaded before the safety caveat. Every clause carries information an agent needs.

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 correctly carries the return-format burden by specifying CSV and its caps. Remaining gap is modest: the slug's semantics and the choice against sibling tools are not covered for this one-parameter tool.

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 0%, so the single 'slug' parameter is documented nowhere else. The description implies slug identifies 'one published dataset' but never states expected format, whether the dataset must be published, or what happens on an unpublished/unknown slug, so it only partially compensates.

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 ('Return a small CSV preview for one published dataset') with concrete scope limits. The role is clearly distinct from siblings like get_dataset_details and generate_loader_code, though no sibling is named explicitly to sharpen the boundary.

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: 'CSV files are not downloaded in full' suggests a lightweight peek before committing to a download or loader generation, but the description never says when to choose this over get_dataset_details or generate_loader_code. No exclusions or prerequisites are given.

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 and openWorldHint=false, so the safety profile is covered by structured data. The description does add genuine non-obvious context: a hard cap of 20 compact records per page, inclusion of source and licence fields, and a prompt-injection warning that dataset text is untrusted. It does not cover pagination limits or auth needs, but the untrusted-data caution is real value beyond the annotations.

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 short sentences, front-loaded with the action and followed by return shape then the security caveat. Every sentence earns its place; nothing is padded.

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?

No output schema exists, and the description does sketch the return shape (compact records with source and licence), which helps. But with five undocumented parameters at 0% coverage and no routing guidance to the sibling tools, an agent still lacks enough to invoke this confidently in context.

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 0% across five parameters, so the description carries the burden and largely fails: query, category, language, and page are never explained. The 'at most 20 compact records per page' line only obliquely signals the page_size maximum (20) already encoded in the schema, leaving the rest undocumented.

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: 'Search the published DesiData catalogue.' That is clearly distinguishable from get_dataset_details and preview_dataset by implication, though no sibling is named explicitly. A clear purpose that stops just short of explicit differentiation.

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 never says when to reach for this tool versus get_dataset_details, preview_dataset, or the generator/notebook siblings, and gives no exclusions. The discovery role is only inferable from the verb 'Search'; nothing is stated about follow-up or fallback.

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