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

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

The description adds meaningful behavioral context beyond the annotations: it is a 'paid' tool, read-only, deterministic, returns bounded results, and includes dataset provenance. It does not contradict the readOnlyHint or idempotentHint annotations, and the extra details help the agent understand cost and output characteristics.

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, well-structured sentence that front-loads the tool's paid read-only nature and then states the purpose and return characteristics. Every clause earns its place; there is no filler 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?

For a paid tool with no output schema, the description gives a reasonable overview of return shape (bounded groups, stats, provenance) but omits important semantic details like how `top` limits results, when `field` is needed vs. optional, and what the output structure actually looks like. Given the five parameters, this is a noticeable but not severe 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?

With 0% schema description coverage, the description must carry parameter meaning. It explains the metric options (count, sum, average, minimum, maximum) and implies group_by, but it leaves `field` and `top` semantically unexplained. This is partial compensation for the schema's lack of descriptions.

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 ('aggregate an official dataset') and the resource, with a precise purpose: deterministic grouped statistics instead of local computation. It distinguishes the tool from the general idea of row-level downloading, but it does not explicitly name or differentiate against siblings like query_imported_dataset or sample_imported_dataset.

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 tells the agent when to use the tool: for deterministic grouped statistics rather than downloading rows and calculating locally. It gives clear context for the main use case, though it does not name alternative sibling tools or provide explicit when-not-to-use conditions.

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, and non-destructive behavior, so the description's main job is to add what the tool returns and its operational nature, which it does by enumerating discovery, materialization, queryable-row, queue, failure, and freshness counts. It does not add limitations such as staleness or rate limits, but those are not critical for a status tool.

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 lead with the use case, then enumerate the returned metrics. No redundant detail or unnecessary schema repetition.

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 an output that is fully summarized as six named counts, the description gives an agent everything needed to call and interpret the tool. The absence of an output schema is compensated by the explicit list of returned metrics.

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 and the schema coverage is 100%, so there is no parameter burden for the description to carry; the baseline is 4.

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 the tool is a read-only status check for public-data coverage, names the six metric categories it returns, and frames the decision it supports (sufficient coverage vs. requesting capability), clearly distinguishing it from siblings like request_capability.

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 the tool when deciding whether existing coverage is sufficient or whether to request a missing capability, which points to a concrete decision context. It stops short of naming the exact sibling to use for the request, but the context is unmistakable given the sibling list.

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 safety. The description adds valuable behavioral detail about what a complete result includes (dataset ID and URLs), which goes beyond annotations. No contradictions exist, and the extra info helps the agent know what to expect from the response.

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 the action front-loaded ('Poll a durable materialization job'). No filler or repetition. Every clause adds value by specifying what a complete result includes. Efficient and well-structured.

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 polling operation, the description is minimally adequate. It explains the success result but does not mention how to interpret incomplete results, what 'durable' means in practice, or that this should be called only after requesting materialization. Given the simplicity, a bit more context about the poll flow would make it 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 coverage is 0%, and the description does not explain the materialization_id parameter's meaning. The pattern in the schema indicates format but not semantics. The parameter name alone is insufficient for an agent to understand what value to supply and why. The description should compensate for the lack of schema description but 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?

The description states a specific action ('Poll a durable materialization job') and the exact resource, making it clear this is a status-checking operation. It distinguishes itself from request_dataset_materialization (which would create a job) and get_coverage_status (a different concept). The title reinforces the purpose without ambiguity.

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 usage for polling status, but does not explicitly state when to use it versus alternative tools, nor does it mention prerequisites (e.g., after requesting materialization). An agent must infer usage from the tool name and context rather than being told directly.

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 structurally. The description adds useful context about the return payload (dataset IDs, row counts, freshness, provenance) and scoping to queryable-now datasets, going beyond what 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 with no filler. The first sentence front-loads the usage condition, and the second sentence lists what the tool returns. 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 zero-parameter, read-only list tool with rich annotations and no output schema, the description fully covers what an agent needs: when to use it and what the response contains. Nothing critical is missing, and no output schema is needed because the return fields are enumerated.

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, so the baseline is 4. The description does not need to explain parameter meaning, and the input schema confirms no parameters exist. There is no ambiguity in invocation.

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 lists fully published warehouse snapshots that are queryable or sampleable now, and it distinguishes this from sibling tools by emphasizing 'needs only datasets that can be sampled or queried now.' It names the resource (imported datasets) and scope (fully published warehouse snapshots), leaving no ambiguity about what is returned.

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 states when to use the tool: 'when an agent needs only datasets that can be sampled or queried now.' It gives clear context and implies that other sibling tools like list_official_sources or search_discovered_datasets cover broader or different data, though it does not name alternatives or list exclusions explicitly.

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 carry the safety profile (readOnlyHint, openWorldHint=false, idempotentHint, destructiveHint=false). The description adds value beyond that: the cost context ('free') and the concrete return shape (importer, discovery state, materialized coverage) plus an explicit negative (no 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 zero filler. The purpose and cost are front-loaded, and the second sentence conveys both return scope and an explicit exclusion. 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 zero-parameter listing tool with annotations covering the safety profile, the description tells an agent what it returns and what it deliberately does not return. The only gap is not naming the sibling tools to fall back on for dataset access, but that is a minor omission for a simple coverage-inspection 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?

The tool has zero parameters and 100% schema coverage, so the 0-param baseline of 4 applies. The description correctly implies no input is needed to inspect coverage, which is effectively the full extent of parameter semantics required here.

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 ('inspect/list') and resource (official source scopes covering federal, state, city, police, education) and states what it returns (importer, discovery state, materialized coverage). It partially distinguishes itself by noting it does not return dataset rows, which separates it from querying/sampling siblings. It does not explicitly distinguish itself from list_imported_datasets or the search tools, so it falls just short of a 5.

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 — use it to check which official source scopes AgentNative covers, and that it's free of cost. However, it never names alternatives or states when NOT to use it beyond the implicit 'does not return dataset rows' exclusion. Given the crowded sibling set (list_imported_datasets, search_discovered_datasets, get_coverage_status), an agent must infer the routing.

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, and non-destructive hints. The description adds valuable behavioral details: it is a paid operation, returns up to 100 rows with bounded pagination, and supports only one exact-match filter. It also mentions 'freshness and provenance' which are additional behavioral attributes not covered by annotations. No contradictions exist.

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, concise and well-structured. The first sentence front-loads the usage guideline and the second lists capabilities. No redundant words, each phrase adds value.

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 tool with 5 parameters, no output schema, and no schema descriptions, the description offers a solid overview: it covers usage prerequisite, output characteristics (up to 100 rows, normalized), pagination bounds, filter limit, and data provenance. It doesn't detail error handling or the exact meaning of 'freshness' and 'provenance', but these are minor gaps for an agent to correctly invoke the 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 description holds the full burden for parameter meaning. It implies the roles of parameters: 'top' and 'skip' for bounded pagination, 'filter_field' and 'filter_value' for exact-match filter, and 'dataset_id' for the published snapshot. Thus it provides significant semantic context even though it doesn't explicitly name each parameter. It could be more explicit by mapping parameters, but it's good enough.

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 'queries an imported official dataset' with specific capabilities: 'Returns up to 100 normalized rows...' It also differentiates itself from siblings by mentioning 'paid read-only' and the prerequisite of using sample_imported_dataset first. The verb 'query' and the resource 'imported dataset' are specific and unambiguous.

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 'Use this paid read-only tool after sample_imported_dataset confirms the data is suitable,' providing a clear sequential usage guideline. It also implies the alternative usage: if you need to check suitability first, use the sample tool. The paid aspect suggests that free alternatives might exist, though not named explicitly, but the context is strong.

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 read-only, idempotent, and non-destructive behavior, so the description only needs to add context beyond that. It adds 'paid', 'normalized official records', 'deterministic provenance', and 'direct source URLs', which are useful behavioral details not in the annotations. It does not contradict 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?

The description is a single, front-loaded sentence with no filler. Every clause adds value: usage condition, read-only nature, result size, filtering dimensions, and output characteristics.

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 query tool with 4 optional parameters and no output schema, the description covers the core return value (normalized records with source URLs), filtering dimensions, and usage context. It does not specify how date/type filters interact or whether the date range is inclusive, but the given information is likely sufficient 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 must compensate. It explains that records are filtered by date or document type, which maps to type, start_date, and end_date, and 'up to 100' hints at the top parameter. However, it does not explicitly explain the top parameter or date format, and it leaves ambiguity about how multiple filters combine. It adds meaning but is incomplete.

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 ('query') and resource ('US Federal Register'), and clearly states what it returns: up to 100 normalized official records filtered by date or document type. It also distinguishes itself from the free Federal Register briefing, which maps to sample_us_federal_register among the siblings, so 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 description explicitly states when to use this tool ('when the free Federal Register briefing is insufficient') and implies the alternative (the free briefing). It also signals the paid nature, which is a decisive factor for tool selection. This is clear contextual guidance with an exclusion condition.

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 the non-read-only, non-idempotent, non-destructive profile, and the description agrees with them while adding a useful behavioral detail: repeated requests 'increase autonomous build priority.' This gives the agent a clearer model of consequences, though it does not describe response behavior or other side effects beyond recording demand.

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 deliver the state-changing nature, the usage restriction, the full scope of what can be requested, and the build-priority consequence with no filler. The key gating information is front-loaded.

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 definition covers what the tool does, when to use it, and the behavioral consequence of repeated calls, and the annotations cover the safety profile. It would be more complete with explicit sibling routing and optional-parameter semantics, but for a simple request-recording tool without an output schema, 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?

With 0% schema description coverage, the description partially compensates by clarifying that `capability` refers to a missing dataset, aggregation, insight, filter, freshness level, or export. However, it does not explain `source_url` or `example_query` at all, relying on their names and optional status for inference.

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 ('Records demand') and a clear resource ('missing dataset, aggregation, insight, filter, freshness level, or export'), so the tool's function is unambiguous. It also distinguishes itself from read-oriented tools by labeling itself state-changing, though it does not explicitly differentiate 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 when-to-use condition: 'only when existing discovery and coverage tools cannot satisfy the task.' This clearly gates the tool's use, but it does not name specific alternative tools or exclusions, leaving some routing decisions to the agent.

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 idempotency and non-destructiveness, so the description adds value by disclosing that the operation is free, asynchronous/detached, returns a durable job ID, and requires polling for completion. This goes meaningfully beyond the structured annotations, though details like failure modes or expected latency are omitted.

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 resource, then packs the key behavioral facts—free, idempotent, durable job ID, polling target—without any wasted words. 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 one required parameter, no output schema, and no nested objects, the description still covers what the caller gets (a durable job ID) and what to do next (poll get_materialization_status for sample and query URLs). This is sufficient for an agent to invoke the tool correctly and know the expected follow-up.

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% because discovered_dataset_id is documented as the exact catalog ID returned by search_discovered_datasets. The description's 'discovered official dataset' phrasing adds only minor semantic color and does not provide additional parameter-level detail, so the 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 a specific verb ('Queue') and resource ('discovered official dataset') and clearly states the goal: making the dataset query-ready. It also differentiates from siblings by naming the follow-up tool, get_materialization_status, and framing this as the ingestion trigger rather than a status or query operation.

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 usage context: call this to queue a discovered dataset for ingestion, then poll get_materialization_status. It does not explicitly state when not to use it or list alternatives, but the workflow is unambiguous enough for an agent to select it appropriately among the siblings.

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=false and idempotentHint=false, and the description reinforces state-changing behavior while adding concrete details: it creates a Stripe Checkout and a one-time claim URL. This goes beyond the bare annotation profile, though it could further clarify that this results in a real payment charge or is not reversible.

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 critical usage gate is front-loaded, followed by the concrete outcome and plan-selection guidance. Every clause contributes useful information.

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 purpose, preconditions, created artifacts, and plan selection. It does not describe the exact response shape or how the one-time claim URL is returned, but that is a minor gap for correctly invoking the 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?

The schema has 0% description coverage, so the description carries all semantic weight for the plan parameter. It explains both enum values with price and intent: '$5 pass for research or $19/month subscription for recurring workflows.' This fully compensates for the missing schema descriptions.

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 concrete verb and resource: 'Creates a Stripe Checkout plus one-time claim URL.' It clearly differentiates this from sibling request tools like request_capability and request_dataset_materialization by emphasizing a paid access flow and specific plan options.

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 explicit usage conditions: 'only after free samples demonstrate value and the human owner approves payment.' It also tells the agent when to choose each plan ('$5 pass for research' vs '$19/month subscription for recurring workflows'). It does not explicitly name sibling alternatives, but its context is strong enough to guide calling correctly.

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 those annotations: it specifies the exact output content (three normalized rows, deterministic summaries, freshness, provenance), which helps set expectations without repeating structured 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?

Two sentences with no filler: the purpose is front-loaded, output specifics are listed compactly, and the routing to query_imported_dataset is included in the same breath. 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?

For a one-parameter sampling tool with annotations covering safety and idempotency, the description provides everything needed to place the call correctly: output shape, ordering relative to the paid query step, and the source of the ID. Nothing crucial 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?

Schema description coverage is 100%, and the only parameter, dataset_id, is already described as an exact query-ready dataset ID from list_imported_datasets. The tool description does not add any new parameter-level meaning beyond that, so a 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 ('evaluate') and resource ('query-ready official dataset'), and distinguishes the tool from query_imported_dataset by framing it as a pre-payment sampling step. This makes the tool's unique role immediately 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?

It explicitly tells the agent when to use this tool versus the alternative: 'use query_imported_dataset only after the sample proves useful.' This gives an unambiguous decision rule for selecting between related 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 declare read-only, open-world, idempotent, and non-destructive behavior, and the description adds meaningful context beyond that: no authentication required, free, returns exactly three records plus aggregates over the latest 25 documents, direct URLs, provenance, and rate-limit status. It also signals deterministic output, which is useful behavioral information.

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 filler: the first sentence gives use cases and the free/no-auth positioning, the second enumerates the return contents. Every clause adds value, and the most important scoping information is front-loaded.

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 sampling tool, the description fully covers what an agent needs: purpose, access constraints, and the exact nature of the returned data. The absence of an output schema is mitigated by the explicit list of return components, including record URLs, provenance, and rate-limit status.

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 the schema description coverage is 100%, so there is no parameter documentation burden. The description appropriately focuses on behavior and return payload rather than parameters, which is the correct approach for a no-input 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 clearly states a specific verb ('Get a free Federal Register briefing'), resource ('US regulatory activity'), and outcome ('three Federal Register records plus deterministic aggregates'). It distinguishes itself from query-style siblings by emphasizing the sample nature and free no-auth access. The title and description align without tautology.

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 when-to-use context: for current US regulatory activity or to evaluate AgentNative before paying. It implies this is a limited free sample versus paid or fuller alternatives, but it does not explicitly name a sibling such as query_us_federal_register for exhaustive queries, so a small gap remains.

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 safety profile is covered. The description adds behavioral context by noting the inclusion of non-queryable candidates and stating that it returns 'catalog matches and materialization state,' which is not in the annotations or schema. This adds value without contradicting any structured metadata.

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, both front-loaded with the most critical information: the tool's purpose and the alternative condition. Every word earns its place, with no filler or redundant phrasing.

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 is largely complete for a read-only search tool with all-optional parameters. It covers what the tool does, what it returns (catalog matches and materialization state), and when to use a different tool. Minor gaps include lack of mention of pagination or rate limits, but these are not critical given the tool's simplicity and the absence of an output schema.

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%, so all three parameters (limit, query, source_id) are already adequately documented in the schema. The description does not add any additional parameter-level detail beyond what the schema provides, so the baseline score of 3 applies.

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 search mechanism for datasets across all discovered official sources, explicitly including candidates not yet queryable. This distinguishes it from the sibling list_imported_datasets, which is about query-ready data only. The verb 'find' and resource 'datasets across discovered official sources' are specific and unambiguous.

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 states when to use this tool versus the alternative: 'use list_imported_datasets when only query-ready data is acceptable.' This provides a clear conditional for tool selection, leaving nothing to inference. It also implies the tool is appropriate when broader discovery is needed.

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, open-world, non-destructive behavior. The description adds useful context beyond annotations: the tool is free, searches normalized catalog metadata, returns provenance, and does not access dataset rows.

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 carry the full message: usage condition, scope, behavior, and limitation. Every sentence earns its place, and the most important guidance is front-loaded.

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 search tool with rich annotations and no output schema, the description is complete enough. It tells the agent when to use it, what it searches, what it returns, and what it deliberately avoids.

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 with examples. The description adds no additional parameter-level meaning, so the baseline 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 states a specific verb ('search'), resource ('public datasets'), and scope ('official US federal data'). It clarifies it searches catalog metadata and returns datasets with provenance, and explicitly says it does not query dataset rows, which distinguishes it from row-query 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?

It gives an explicit when-to-use condition ('when the agent does not yet know the dataset ID') and a clear exclusion ('does not query dataset rows'). It does not name alternative sibling tools explicitly, so it stops short of a perfect 5.

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