ClinicMarketIQ Public Data

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

Annotations already establish read-only, idempotent, and non-destructive behavior, and the description is consistent with them. It adds context that the index is canonical, public, and includes an immutable manifest pointer, but it does not disclose error behavior, response shape, or access constraints beyond that.

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 sentence front-loads the action and object, and every modifier adds relevant specificity. There is no filler, redundancy, or repetition of schema details.

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 one required parameter and no output schema, the description provides enough orientation about what the tool returns and for which scope. It does not define the exact return format or error handling, but the annotations, simple parameter surface, and sibling context keep that gap moderate.

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 description reinforces the dataset-family concept but adds no additional meaning beyond the schema's example and catalog-slug option. Baseline 3 is appropriate because the schema carries the parameter semantics.

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 clear operation ('Fetch') and a specific resource: the canonical public machine index for one ClinicMarketIQ dataset family. It signals scope and content sufficiently to distinguish it from listing/search siblings, though it does not explicitly name them.

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?

Use is implied rather than stated: this is the metadata/index tool for a single dataset family, not for listing, searching, resolving download URLs, or verifying checksums. There is no explicit when-to-use or when-not-to-use guidance, so the agent must infer routing from the wording and sibling names.

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 read-only, idempotent, non-destructive behavior. The description adds behavioral value beyond those flags: it says the checksums are 'declared' rather than verified, the URLs are 'canonical' and direct, and the tool 'never proxies' bytes. No contradiction with 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 focused sentences, with the primary result front-loaded and the key non-behavior stated immediately after. Every clause 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 no output schema, the description should describe not just the high-level return content but enough about the optional parameters and expected result structure to support correct invocation. It does not explain group, leaves format semantics ambiguous, and does not relate this tool to verify_checksum, leaving gaps in a simple but incompletely specified API.

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 only 33% (only dataset is explained), and the description adds no parameter-level meaning for group or format. While format has an enum and default in the schema, its purpose and the meaning of 'any' are not clarified, and group is entirely unexplained.

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 operation ('Return canonical direct download URLs and declared SHA-256 values') on a specific resource ('a public dataset') and adds a sharp boundary ('never proxies the dataset bytes'). This distinguishes it from sibling metadata/list/search tools even without naming them.

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 text implies the tool is for retrieval of direct download URLs and checksums, but it does not explicitly state when to prefer it over siblings such as verify_checksum, nor does it state exclusions like 'use only for public datasets' or 'do not use to fetch file contents.' Usage context is clear but guidance is implied, not explicit.

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 read-only, idempotent, non-destructive behavior. The description adds meaningful context: the manifest is immutable and public, and omitting schema_name returns all schemas with a current cap of 10. No contradiction with 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 tightly scoped sentences with no filler. The primary action is front-loaded, and the second sentence adds a behaviorally important edge case without redundancy.

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 tool with two parameters and no output schema, the description covers the key calling behavior and the main optional-parameter nuance. It stops short of describing the return JSON shape, but given the tool name and purpose that is 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?

The description clarifies that schema_name is optional and explains the behavior when omitted, which the input schema leaves undocumented. However, it does not add detail about schema_name's meaning or value format, so it only partially compensates for the 50% schema description coverage.

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 ('Fetch') and resource ('schema JSON declared by a dataset's immutable public manifest'), making the tool's function immediately clear. It is visually and semantically distinct from siblings like get_dataset_metadata and get_download_url.

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 purpose implies when to use it: whenever a caller needs schema JSON for a dataset. However, it does not explicitly compare against alternatives or state when not to use it, leaving routing to inference.

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 mark this read-only, idempotent, non-destructive, and open-world; the description adds that results are metadata summaries rather than observation rows, a useful behavioral clarification.

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 tightly worded sentences, front-loaded with the core action and a single clarifying detail; no filler.

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 paginated list tool, the description plus annotations cover safety and output type; missing only explicit pagination guidance and sibling routing, which are minor.

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 says nothing about limit/offset semantics, defaults, or pagination, so it does not compensate for the missing parameter documentation.

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 exact resource ('public ClinicMarketIQ dataset families from the canonical catalog') and clarifies the output ('metadata summaries, not observation rows'), which separates it from row-returning tools.

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 context of 'canonical catalog' implies use for browsing available public datasets, but there is no explicit when-to-use or contrast with siblings like search_datasets or get_dataset_metadata.

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 readOnly, idempotent, and non-destructive behavior, so the safety burden is handled. The description adds useful scope ('public') and the searchable fields, but it does not disclose pagination, matching behavior, ordering, or return shape.

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, focused sentence with no filler. The verb and scope are front-loaded, making the core behavior immediately clear.

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 two-parameter search tool with strong annotations, the definition is usable but incomplete. It lacks sibling differentiation and any hint about result format, and with no output schema those details would add meaningful 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%, so the description must compensate. Listing the searched fields gives meaning to the query parameter, but limit is left to its name and schema constraints, which are fairly self-explanatory. Partial compensation 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?

States a specific verb ('Search'), a resource ('public dataset family IDs, names, slugs, publishers'), and a scope ('canonical ClinicMarketIQ catalog'). However, it does not explicitly differentiate this from list_datasets, leaving some distinction to inference.

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?

No when-to-use guidance or alternatives are provided. The agent must infer that search_datasets is for query-based lookup rather than enumeration via list_datasets, and no exclusions are 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 already declare readOnly, idempotent, and non-destructive behavior, so the bar for extra disclosure is lower. The description adds valuable behavioral context beyond annotations by stating that the check is bounded and does not download or re-hash large artifact bytes, which helps agents understand cost and side effects.

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 tight sentences with no filler. The core comparison action is front-loaded, and the bounded/no-download behavior is stated immediately after, making the most decision-relevant information prominent.

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 verification tool with rich annotations, the description covers the operation, the data source, and a key performance characteristic. It does not describe the return value or mismatch behavior, but the tool name and 'verify' semantics make the expected outcome reasonably inferable.

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 only 33%, so the description must compensate for undocumented parameters. It meaningfully clarifies sha256 as the caller-provided digest and dataset as being indexed in the public machine index, but filename semantics are still left implicit and not described in either the schema or the description.

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 verb and resource: compare a caller-provided SHA-256 digest against the canonical digest in the public dataset machine index. This clearly distinguishes the tool from sibling metadata, download, schema, and search tools, and the title reinforces the purpose.

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 clear context for when to use this tool: for checksum verification without downloading or re-hashing artifact bytes. It does not explicitly name alternative tools or exclusion conditions, but the bounded nature of the check implies the intended use case clearly.

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