AgentNative: Public Data, Government Datasets, Federal Statistics & Official Records

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

Annotations already communicate read-only, idempotent, and non-destructive behavior. The description adds useful context beyond those annotations: the tool is paid, deterministic, returns bounded results, and includes dataset provenance. This is valuable without contradicting any 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 no filler. The primary use case is front-loaded, and the sentence about return behavior adds necessary detail 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?

The core purpose and return type are described, but with 5 parameters, 0% schema coverage, and no output schema, the definition does not fully equip an agent to call the tool correctly. Missing details about top bounds, field requirements, and metric-specific behavior are notable gaps.

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, but it mostly restates metric enum values ('count, sum, average, minimum, or maximum') and the grouping concept. It does not explain top, field, dataset_id, or the dependency between metric and field, leaving important parameter semantics 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?

The description clearly states the tool's purpose: deterministic grouped statistics instead of downloading rows and calculating locally. It makes the aggregation focus obvious, though it does not explicitly name sibling tools like query_imported_dataset or sample_imported_dataset as the row-returning alternatives.

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 gives a clear usage context: choose this tool when you need grouped statistics and want to avoid local computation. However, it does not explicitly state when not to use it or point to which sibling tools should be used for raw-row retrieval or sampling.

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, idempotentHint, and destructiveHint false, covering the safety profile. The description adds value by specifying the output categories it returns (discovery, materialization, queryable-row, queue, failure, freshness), which is especially useful given there is no output schema. No contradictions 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 sentences, each earning its place: the first states the use case and key positioning ('free operational'), the second lists the returned metrics. No redundancy or 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, parameterless status tool, the description provides the purpose and a summary of the returned counts. It does not detail the format of each count or a formal schema, but given the low complexity and helpful annotations, this is sufficient for an agent to select and 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?

The tool has zero parameters and schema coverage is 100%, so there are no parameter semantics to explain. The description correctly focuses on purpose and output. Baseline 4 is appropriate for a parameterless tool.

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 verb and resource: get public-data coverage status. It also explains the decision this tool enables ('decide whether available public-data coverage is sufficient or whether to request a missing capability') and names the returned metrics, making its purpose unambiguous and distinguishable from siblings like request_capability or get_materialization_status.

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 tells the agent when to use this tool: when evaluating whether coverage is sufficient or a capability is missing. It also signals that it is 'free operational', implying it should be used before paid or request-based alternatives. However, it does not explicitly name sibling alternatives or state when not to use it.

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 idempotentHint=true, covering the safety profile. The description adds value beyond that by explaining the 'durable' nature of the job and setting expectations that a complete result includes a query-ready dataset ID and sample/paid query URLs. 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 sentences, no filler. The verb and resource are front-loaded, and the second sentence efficiently clarifies what a successful poll looks like. 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?

For a simple one-parameter polling tool with rich annotations, the description is largely sufficient. The main gaps are the lack of explicit mention of in-progress statuses/error handling and the absence of guidance on polling intervals, but the tool's low complexity reduces the severity of these omissions.

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 carry the burden for parameter meaning. It implies that materialization_id identifies the job to poll, but it never explicitly names or defines it. It also doesn't describe possible intermediate statuses (e.g., pending, in-progress, failed), leaving some ambiguity about what the parameter's polling target returns.

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 ('Poll') and a specific resource ('a durable materialization job'), making the tool's purpose unmistakable. It also distinguishes it from siblings like aggregate_imported_dataset, get_coverage_status, and search_discovered_data, which concern different operations or resources.

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 provides clear context: this tool is for polling the status of a durable materialization job, which implies a long-running async process. It does not, however, explicitly state exclusions or name alternative tools for querying the resulting data directly, so it falls short of 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 provide readOnly, idempotent, and non-destructive safety traits. The description adds useful behavioral context by specifying that it returns 'fully published warehouse snapshots' with dataset IDs, row counts, freshness, and provenance, which helps the agent understand what the response contains.

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 concise sentences, with the core usage condition front-loaded. Every sentence adds value: the first tells when to call it, the second tells what it returns. No wasted words.

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 zero-parameter, read-only listing tool, the description is complete. It covers purpose, usage condition, and return contents, and the sibling list provides surrounding context. No output schema exists, but the description gives enough return-value information.

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?

There are zero parameters, so the baseline is 4. The description has nothing to add about parameters and does not need to; the input schema already covers the complete parameter set.

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 ('list') and a precise resource ('datasets that can be sampled or queried now'). It clearly distinguishes itself from siblings by narrowing scope to queryable/sampleable official datasets only, rather than all discovered or official sources.

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 explicitly states when to use the tool: 'Use this free tool when an agent needs only datasets that can be sampled or queried now.' It does not name alternative tools directly, but the 'only' condition and the sibling tool names make routing fairly clear.

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. The description adds meaningful behavioral context beyond those: it is a free tool, it returns per-source importer/discovery/materialized coverage, and it explicitly excludes dataset rows. 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 sentences with no filler. The primary purpose is front-loaded, and the second sentence adds exact return fields plus a useful exclusion. 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?

With no parameters and no output schema, the description fully specifies the return contents (importer, discovery state, materialized coverage) and the key non-return (dataset rows). This is sufficient for an agent to decide whether to call this tool and what to expect from 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?

This tool has zero parameters, so the baseline is 4. The description does not need to explain parameter meaning, and it correctly focuses on the output semantics instead.

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 ('inspect') and resource ('official source scopes AgentNative currently covers'), and immediately clarifies what the tool returns and what it does not return. It clearly differentiates from siblings like list_imported_datasets by emphasizing scope coverage rather than dataset rows.

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 use: when you need to know which official source scopes are covered and their importer/discovery/materialization state. It does not explicitly name alternative tools or provide when-not-to-use guidance, but it disambiguates by stating that dataset rows are not returned.

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 idempotentHint=true, so the description does not need to restate read-only behavior. It adds valuable behavioral context beyond annotations: it is a paid tool, returns at most 100 rows, has bounded pagination, and supports one exact-match filter. This goes beyond what the annotations provide.

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 critical usage gate and paid read-only nature, followed by a compact enumeration of behavior. Every phrase earns its place and there is no redundancy with annotations 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?

For a read-only query tool with 5 parameters and no output schema, the description gives the essential selection and invocation context: when to use it, row limit, pagination bound, filter behavior, and provenance/freshness. It could mention output shape or error behavior, but the core decision-making information is present.

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 carries the burden. It adds meaning for filter_field/filter_value ('one exact-match filter') and for top/skip ('up to 100', 'bounded pagination'), but dataset_id is not explicitly explained and skip semantics are only implied. The description partially compensates for the schema gap but not fully.

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 verb (query), resource (imported dataset), and scope: returns up to 100 normalized rows from a published snapshot. It also distinguishes itself from sample_imported_dataset by saying it runs after that tool confirms suitability, and from aggregate_imported_dataset by describing row-level output.

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 explicitly tells the agent to use this tool after sample_imported_dataset confirms the data is suitable, which is clear contextual guidance. It does not explicitly list exclusions or when to prefer aggregate_imported_dataset, but the usage context is strong enough for a 4.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description's main job is to add context beyond those. It adds the paid requirement, the 100-record cap, the normalization of records, deterministic provenance, and direct source URLs—all useful behavioral details not present in 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?

One dense sentence carries the trigger condition, cost context, result cap, filter dimensions, provenance guarantees, and output format. Every clause earns its place and the most important guidance—when to use—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?

There is no output schema, so the description should describe return shape, and it does: up to 100 normalized official records with direct source URLs and deterministic provenance. It does not list specific returned fields or clarify behavior when no filters are provided, but for a read-only query tool with this parameter set, the guidance is reasonably complete.

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 for silent parameters. It maps 'up to 100' to the top parameter, 'document type' to the type enum, and 'date' to start_date/end_date. It does not explain whether dates are inclusive or how top behaves when omitted, but the schema itself provides formats and bounds.

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 ('query'), a resource ('US Federal Register'), and the scope ('returns up to 100 normalized official records filtered by date or document type'). It clearly differentiates itself from the free Federal Register briefing and sibling tools like sample_us_federal_register.

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 explicitly says to use this paid tool when the free Federal Register briefing is insufficient, giving a clear trigger condition and implying the free alternative should be used otherwise. However, it does not name sibling tools explicitly or explain when to prefer this over query_imported_dataset or list_official_sources.

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?

The description states the tool is state-changing and discloses the key behavioral consequence: repeated requests increase autonomous build priority. This goes beyond the annotations, which only flag readOnlyHint=false and idempotentHint=false, by explaining how requests accumulate. It does not cover authentication, persistence, or response behavior, but enough is added to be useful.

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. It front-loads the critical usage gate ('only when existing discovery and coverage tools cannot satisfy the task'), then immediately enumerates the supported capability types and the demand-accumulation behavior.

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 description gives enough context for an agent to decide when to call the tool, what it does, and what repeated calls mean. It omits explicit guidance on optional parameters and sibling disambiguation, but for a request-recording tool with a simple schema, this is a solid and mostly complete definition.

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. It does cover the required parameters conceptually by listing dataset and capability types, but it gives no explanation of the optional source_url or example_query parameters. Those remain implicit and left to inference from their names.

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 clearly identifies the tool as a state-changing request that records demand for missing capabilities, and enumerates concrete capability types (dataset, aggregation, insight, filter, freshness, export). It distinguishes itself from discovery and coverage tools, but does not explicitly differentiate itself from sibling request tools like request_dataset_materialization or request_paid_access.

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 an explicit usage condition: use only when existing discovery and coverage tools cannot satisfy the task. This provides a clear threshold, but it does not name specific alternative tools or state when request_capability should be avoided in favor of a specific sibling.

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?

The description adds valuable behavioral context beyond the annotations: the operation is free, asynchronous, durable (returns a job ID), and requires polling until sample/query URLs are available. It discloses the detached ingestion nature and the need to check status, which significantly exceeds what annotations alone provide. 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 sentences with zero fluff. The core action is front-loaded, the return value is stated, and the next-step polling instruction is included. Everything 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?

For a single-parameter async queueing tool with no output schema, the description is complete: it names the required identifier, the operation's effect, the return value, and the polling endpoint. An agent has enough information to invoke the tool correctly and know what to do next.

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 input schema provides 100% coverage with a clear description of discovered_dataset_id as an exact catalog ID returned by search_discovered_datasets. The tool description does not add extra parameter-level detail, but given full schema coverage, baseline 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 states a specific verb ('Queue') and resource ('discovered official dataset'), plus the operational effect: prioritized detached ingestion. It also names the follow-up tool (get_materialization_status), which distinguishes this tool as the request/trigger step versus the polling step.

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 clearly implies the workflow: after discovering a dataset, queue it for materialization; then poll get_materialization_status for sample and query URLs. It does not explicitly state when not to use this tool versus alternatives, but the polling instruction provides solid context and routes the agent to the correct sibling.

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 as non-read-only and non-idempotent; the description adds concrete external behavior: it creates a Stripe Checkout and a one-time claim URL, and requires human approval before use. This goes beyond the structured fields.

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 no filler. The precondition and state-changing warning are front-loaded, followed by the created artifacts and plan options. Every phrase 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?

For a single-parameter tool with no output schema, the description covers when to use it, what it creates, and how to choose the plan. Nothing essential is missing for an agent 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?

The only parameter, plan, is fully explained in prose: $5 pass for research, $19/month subscription for recurring workflows. Since schema description coverage is 0%, the description fully compensates by giving each enum value meaningful context.

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?

Description names a specific action (creates a Stripe Checkout plus one-time claim URL), identifies the tool as state-changing, and clearly differentiates it from the data/import/query siblings. The title and body align on the paid-access resource.

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?

Explicitly states the prerequisite: use only after free samples demonstrate value and the human owner approves payment. It also guides plan selection by tying the 'pass' option to research and the 'subscription' option to recurring workflows.

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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral detail beyond annotations: it returns three normalized rows, deterministic summaries, freshness, and provenance, and frames sampling as an evaluation-only step.

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 concise, front-loaded with the core value proposition, and uses a second sentence to clarify the follow-up tool. Every sentence earns its place with minimal 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 single-parameter, read-only tool with rich annotations, the description is complete. It explains what the output contains, when to use the tool, and what to do next, so an agent has enough context 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?

Schema description coverage is 100%, and the dataset_id parameter is well documented as an exact query-ready dataset ID from list_imported_datasets. The description itself does not need to add much about the parameter, so baseline 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 clearly states the tool samples a query-ready official dataset for free evaluation before payment, with a specific verb, resource, and tangible outputs. It also names the sibling query_imported_dataset, distinguishing this tool's purpose from that alternative.

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 explicitly says to use this tool before paying and to use query_imported_dataset only after the sample proves useful, providing clear when-to-use guidance. It does not explicitly exclude specialized siblings like sample_us_federal_register, but the overall context is clear enough for the primary workflow.

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?

The description goes beyond the read-only/idempotent annotations by disclosing exact output composition: three Federal Register records, deterministic aggregates over the latest 25 documents, direct record URLs, provenance, and rate-limit status. This gives the agent a clear picture of what the call returns and how it behaves.

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 main use case, and packed with concrete output details. No wasted words or redundant restatement of the tool name or annotations.

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 no-parameter sampling tool with no output schema, the description tells the agent why to use it, what it returns, and what operational details matter (rate-limit status). Nothing essential 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?

There are zero parameters and schema coverage is 100%, so the schema requires no explanation. The description still adds relevant context about the fixed nature of the output, which is sufficient for a parameterless tool.

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 purpose: sampling current US regulatory activity via a free, no-auth tool. It clearly distinguishes itself from paid/full-query siblings by emphasizing the free, no-auth, three-record sample output.

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 explicitly says when to use the tool: for current US regulatory activity or to evaluate AgentNative before paying. It does not name sibling tools explicitly as alternatives, but the 'before paying' framing implies the boundary against paid access.

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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond annotations: the search spans candidates not yet queryable and returns materialization state, which helps the agent predict results without assuming all matches are query-ready.

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 sentences with no wasted words. It front-loads the core purpose, then adds return behavior and the key alternative, making it easy for an agent to parse quickly.

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 read-only search tool with no required parameters and no output schema, the description provides everything needed: purpose, scope, return content, and the alternative when query-ready data is required. The annotation set covers safety, so nothing critical 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?

The input schema covers all three parameters with meaningful descriptions, so the description does not need to repeat parameter details. The description adds no extra parameter semantics, which is acceptable given 100% schema 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 clearly states a specific action ('find datasets') on a specific resource ('all discovered official sources'), and clarifies the unique scope by including candidates not yet queryable. It also names what the tool returns ('catalog matches and materialization state'), distinguishing it from list_imported_datasets without needing to open 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 explicitly names the sibling tool list_imported_datasets and gives the exact condition for choosing it instead ('when only query-ready data is acceptable'). This directly tells an agent when to use this tool versus an alternative, leaving no ambiguity.

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 the tool as readOnly, idempotent, and non-destructive; the description adds meaningful behavioral context by noting it is free, searches normalized metadata, returns provenance, and avoids querying dataset rows. This usefully frames cost and scope beyond what annotations state.

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 written sentences that front-load the most important usage guidance and follow with scope boundaries. Every sentence adds value, and there is no repetition of schema or annotation 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?

For a single-parameter, read-only search tool, the description is complete: it explains when to use it, what it searches, what it does not do, and what kind of results are returned. The existing annotations cover safety semantics, so no further behavioral disclosure is needed.

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 schema already describes the query parameter well with examples. The description reinforces plain-language usage but does not materially add meaning beyond the schema. Baseline 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?

States a specific verb ('searches') and resource ('normalized catalog metadata') and clearly distinguishes itself from row-level query tools. The phrase 'does not query dataset rows' removes ambiguity about what kind of search this performs.

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 explicit situational guidance: use first when an agent needs official US federal data but does not yet know the dataset ID. It also conveys a boundary by stating that it does not query dataset rows, implying row-level tools should be used instead. It does not name a specific alternative, but the context is strong enough.

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