Dynamik Public Data

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful context by naming the metadata categories returned, including explicit access-tier disclosure, but says nothing about failure behavior when a dataset is unknown or the caller lacks account/paid entitlement.

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?

One dense sentence with the resource and the informative payload front-loaded and no filler. The list of attributes borders on enumerating return fields that the output schema already covers, but it is compact enough to earn its place.

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?

An output schema exists, so return values need not be explained, and annotations cover the safety profile. However, for a tool whose only input is an opaque id and whose nearest sibling has a near-identical name, the description leaves both id sourcing and sibling differentiation unaddressed.

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% and the single required parameter dataset_id is never mentioned in the description. It gives no hint about the identifier format, where such ids come from (presumably search_datasets), or whether slugs are accepted, so it fails to compensate for the coverage 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 gives a specific verb ("read") and resource (a dataset's metadata) and enumerates exactly which attributes are surfaced: freshness, coverage, provenance URLs, limitations, and access status. It does not distinguish itself from the similarly named sibling describe_dynamik, leaving ambiguity about which tool returns what.

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 when-to-use guidance, no prerequisites, and no mention of alternatives such as describe_dynamik or search_datasets. The agent must infer that this is a metadata lookup rather than a sampling or search operation.

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 read-only, idempotent, non-destructive behavior in a closed world, so the bar is lower; the description still adds real context by disclosing that this yields a connection_handoff intended for the user and that the tool list changes afterward ('refresh tools and continue'). That is genuine side-effect information beyond the annotations. It doesn't explain what happens if the user declines the connection, which keeps it off 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 action and its output, followed by compact conditional guidance. No filler, and the most important information (what you get back) comes first.

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 an output schema present, the return shape needn't be spelled out, and the description still names the key artifact (connection_handoff) and the follow-up procedure. It is complete enough to invoke correctly, though it could say more about what 'Dynamik Studio' is for an agent unfamiliar with it.

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 takes zero parameters, so there are no parameter semantics to convey — the baseline applies. The description correctly spends no space on argument details.

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-plus-resource combination ('Discover Dynamik Studio') and states the concrete result ('get a clickable connection handoff'), which is clearly distinct from the dataset-oriented siblings. It does not explicitly contrast itself with those siblings, but the resource is different enough that an agent can tell them apart.

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 conditional 'If the requested task needs Dynamik Studio' gives a clear trigger, and the follow-up steps (show handoff, help connect, refresh tools, continue) define the expected workflow. It stops short of saying when *not* to call this or naming an alternative, so it falls below a 5.

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 specify read-only, idempotent, non-destructive, and closed-world behavior. The description adds important context beyond annotations: the URL is bounded, eligibility depends on the canonical catalog marking the release public, and the tool does not proxy dataset content. This meaningfully clarifies operational boundaries.

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 two tightly written sentences with no wasted text. The action and gating condition are front-loaded, followed immediately by an important non-behavioral caveat.

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?

The tool has rich annotations and an output schema, so the description does not need to explain safety or return values. It covers the critical public-release gating and non-proxy behavior. The main remaining gap is parameter-level meaning, which the schema partially covers through types, defaults, and an enum.

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 description should compensate for undocumented parameters. It does not mention dataset_id, count, or format at all. The schema itself supplies names, types, defaults, enum values, and bounds, but the description adds no parameter meaning.

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 states a specific action and object: it builds a bounded request URL for the current release. It also clarifies the public-release condition and that it does not proxy dataset content. It does not explicitly differentiate itself from siblings like search_datasets or describe_dataset, but the core purpose is clear.

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 a clear precondition: use only when the canonical catalog marks the current release public. It also says the tool does not proxy content, which implies a non-retrieval use case. However, it does not name alternatives or explain when to use this versus sibling tools.

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 cover the safety profile exhaustively (readOnly, idempotent, non-destructive, closed-world), lowering the bar. The description does add genuinely useful context beyond them: that results only declare release availability and confer no entitlement. But it omits search behavior such as matching rules, result limits, or what an empty query returns.

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, no filler, with the scope constraint front-loaded. Nothing is redundant with the title or schema.

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?

An output schema exists, so return-value documentation is not required, and the entitlement caveat is a valuable addition. Still, for a search tool with an undocumented free-text parameter and no alternatives guidance, key calling details remain unaddressed.

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% and the single `query` parameter has no description in the schema. The description never mentions the parameter at all — no syntax, no matching semantics, no note about matching fields (name vs. metadata). With low coverage the description was required to compensate and 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 (search) and resource (Dynamik dataset metadata) with a meaningful scope qualifier ('public-safe'). It does not, however, distinguish itself from siblings like describe_dataset or get_dataset_sample_link, so an agent must infer 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?

The description says nothing about when to use this tool versus describe_dataset, describe_dynamik, or get_dataset_sample_link. The second sentence is a caveat about interpreting results, not guidance on selecting or invoking the tool.

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