Dynamik Public Data

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it is safe. The description adds value by listing the specific metadata categories (freshness, coverage, provenance, limitations, access status), which goes beyond the generic safety profile. It does not contradict any annotation and provides contextual detail about the tool's 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?

The description is a single, compact sentence that immediately states the action and lists the key attributes. There is no fluff or redundancy, and the critical information is front-loaded. It earns its place without verbosity.

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 takes a single simple parameter, has a declared output schema (which covers return values), and annotations cover safety semantics. The description adequately specifies what metadata is described. It does not mention error handling or edge cases (e.g., missing dataset), but for a read-only retrieval tool with a well-defined output schema and annotations, this is sufficient. A 5 would require covering additional operational details that are not essential here.

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 schema has only one parameter, dataset_id, which is self-explanatory and requires no additional semantics. The description does not explicitly mention dataset_id, but the phrase 'a dataset’s' implicitly signals that a dataset identifier is needed. With 0% schema coverage, the description could have elaborated on how to format the ID, but given the simplicity of the parameter and the clear resource reference, a baseline score of 3 is 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 uses the specific verb 'Read' followed by the resource 'a dataset’s' and enumerates the concrete attributes (freshness, coverage, provenance URLs, limitations, access status). This clearly distinguishes it from siblings: search_datasets is for finding datasets, get_dataset_sample_link retrieves a sample, and describe_dynamik likely covers a different scope. The purpose is unambiguous even without consulting the 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?

The description implies when to use the tool (when you need dataset metadata), but it does not explicitly state when to avoid it or mention alternatives. There is no reference to sibling tools or exclusions, leaving the agent to infer the context. This meets the 'implied usage' level but not the 'clear context with exclusions' bar.

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 readOnly, idempotent, non-destructive behavior, and the description adds genuinely new context: it returns a connection_handoff, requires the user to authenticate in their agent host, and tells the agent to refresh tools afterward. This auth-requirement disclosure is exactly the kind of trait annotations don't cover.

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 purpose before the workflow instruction. The second sentence packs multiple steps but each is actionable and earns 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?

With an output schema present, the description need not document return values, and it appropriately explains how to use the returned handoff instead. What an agent needs to call and follow up correctly is covered.

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 no parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly spends no words on inputs.

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 ('Discover Dynamik capabilities') and names a concrete artifact produced ('clickable connection handoff'). It doesn't distinguish itself from siblings like describe_dataset or search_datasets, but the purpose is unambiguous on its own.

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 account tools...' implies when this tool matters, but it is framed as post-call workflow rather than as guidance for choosing this tool over its siblings. No explicit alternatives or exclusions 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 readOnly, idempotent, and non-destructive. The description adds that it only builds a URL (no content proxying) and conditions on public release status. This is beyond what annotations provide, and no contradiction is present.

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 with zero fluff. The core action and condition are front-loaded. Every word earns 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 are covered. However, the description omits what to do if the release is not public (e.g., error handling) and does not explain parameter semantics. For a tool with 3 parameters and no schema coverage, more guidance is needed for correct invocation.

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 clarify the parameters. It mentions 'bounded request URL' but does not explain how count, format, or dataset_id affect the URL. Without this, an agent cannot correctly construct meaningful requests beyond default values.

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 (build a bounded request URL), the resource (dataset sample link), and a key condition (only when release is public). It also clarifies a non-behavior (does not proxy content), distinguishing it clearly from siblings like describe_dataset 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?

Provides clear context for when to use: only when the canonical catalog marks the release public. It also gives an exclusion (does not proxy dataset content), implying it's not for fetching content. It doesn't name alternatives explicitly, but the condition is 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: results are public-safe, indicate release availability, and do not imply account entitlement. This helps set expectations without contradicting 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 short sentences with no filler. The primary action and key qualification are front-loaded, and the entitlement caveat earns its place by preventing incorrect interpretation of results.

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?

Given a simple optional query, strong safety annotations, and an existing output schema, the description covers the essential behavioral aspects. The main missing element is routing guidance relative to sibling tools, but this is not critical for a straightforward metadata search.

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 description does not explain what 'query' means beyond the implicit search context. It does not state whether query is free-text, supports exact names, or accepts partial matches. With only one optional parameter, the gap is modest but still present.

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 ('Dynamik dataset metadata'), clearly distinguishing it from the sibling describe/get tools. It also adds a useful qualifier ('public-safe') that clarifies scope immediately.

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 implies this is the tool for searching metadata, but it does not explicitly state when to use it instead of describe_dataset, describe_dynamik, or get_dataset_sample_link. There is no when-not guidance or mention of alternatives.

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