Official data with free samples, provenance, aggregations, freshness and agent-ready insights.
Average 4.3/5 across 13 of 14 tools scored. Lowest: 3.6/5.
Each tool maps to a distinct lifecycle stage: discovery, materialization, sampling, querying, aggregation, and payment. The main ambiguity is between search_public_datasets and search_discovered_datasets, plus some overlap between get_coverage_status and list_official_sources, but the descriptions provide enough guidance for most selections.
All tools use a consistent verb_object snake_case pattern with clear verbs: get_, list_, query_, request_, sample_, search_, and aggregate_. State-changing actions uniformly use request_, and status reads uniformly use get_.
With 14 tools, the server is well within the ideal range and each tool earns its place across the data lifecycle: discover, materialize, sample, query, aggregate, and manage access. The count feels complete without being padded.
The set covers discovery, materialization, sampling, querying, aggregation, coverage status, and paid access, with provenance embedded throughout. Minor gaps exist around the 'insights' promised in the server name and lifecycle operations like cancellation or removal, but agents can generally complete core workflows.
aggregate_imported_datasetAggregate an official datasetAUse this paid read-only tool for deterministic grouped statistics instead of downloading rows and calculating locally. Returns bounded count, sum, average, minimum, or maximum groups with dataset provenance.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| field | No | ||
| metric | No | ||
| group_by | Yes | ||
| dataset_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds key context: it is paid, deterministic, and bounded (via the top parameter), plus mentions dataset provenance. This goes beyond the annotations and offers useful behavioral information without contradiction.
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 very concise: two sentences, front-loading the purpose and key differentiator. No fluff, every word contributes to understanding the tool's role.
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 5 parameters, 2 required, and no output schema, the description is insufficient for an agent to correctly construct a call. It lacks parameter semantics, does not explain the relationship between fields, and omits return structure details. The agent would be left guessing on how to set 'field', 'top', and how the result is formatted.
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 does not explain any parameter. It only lists metric types (count, sum, avg, min, max) but does not tie them to the 'metric' parameter. There is no explanation of 'dataset_id', 'group_by', 'field', or 'top'. This is a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('aggregate') and resource ('imported dataset'), and clearly states the output is grouped statistics (count, sum, average, min, max) with dataset provenance, distinguishing it from local computation. It effectively communicates the tool's core function.
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 recommends this tool 'instead of downloading rows and calculating locally', providing a clear usage scenario. However, it does not mention when not to use it or compare with sibling tools like query_imported_dataset, which could be more explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_coverage_statusGet public-data coverage statusAUse this free operational tool to decide whether available public-data coverage is sufficient or whether to request a missing capability. Returns discovery, materialization, queryable-row, queue, failure, and freshness counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a read-only, idempotent, non-destructive operation, so the description doesn't need to restate these. It adds value by disclosing that the tool returns various counts (discovery, materialization, etc.), which informs the agent of the data shape. It does not mention potential rate limits or cost, but states it is 'free,' which is useful. 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?
The description is two sentences, front-loaded with the purpose, and directly lists the return categories. Every clause earns its place without redundancy. It is concise 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 no-parameter, no-output-schema tool with strong annotations, this description is largely complete: it explains the raison d'être, the decision it supports, and the data it returns. It could potentially define what constitutes 'sufficient' or provide interpretation guidance for the counts, but that is not strictly required for an agent to call 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 tool has zero parameters, and the schema coverage is effectively complete since there are no properties to describe. Per the baseline rule for tools with no parameters, a score of 4 is appropriate; the description does not need to explain behavior of parameters that don't exist.
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: to decide whether public-data coverage is sufficient or if a capability should be requested. It explicitly lists the return values, making its function specific and distinguishable from generic 'status' tools. However, it does not explicitly compare itself to sibling tools like get_materialization_status, leaving some ambiguity for an agent to differentiate based on the word 'coverage'.
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 a clear use case: to decide whether to request a missing capability, which implies it should be used before issuing a request. This frames when to invoke it but does not explicitly list exclusions or contrast with alternatives like request_capability. A hint about when not to use it (e.g., when checking a specific dataset's materialization) would strengthen this, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_materialization_statusCheck dataset materializationAPoll a durable materialization job. A complete result includes the query-ready dataset ID plus free sample and paid query URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| materialization_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description does not need to cover safety. It adds useful context by stating that a complete result contains a dataset ID and sample/paid query URLs, but it does not describe interim statuses or what happens if the job is still running or fails.
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 that states the action, then a compact second sentence that adds the key result contents. There is no wasted wording.
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 no output schema, the description gives the main purpose and the shape of a complete result. However, it omits the status response of incomplete jobs, which is critical for a polling loop, and does not mention error/failure behavior.
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 needed to compensate, but it only implies that materialization_id identifies the job being polled. It adds no detail about where the ID comes from, format expectations, or relationship to the response fields.
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 and resource: 'Poll a durable materialization job,' and clarifies the output by listing the query-ready dataset ID plus free sample and paid query URLs. This distinguishes it from request_dataset_materialization and get_coverage_status, so an agent can select it without opening 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 phrase 'Poll a durable materialization job' implies this is used after a materialization request, but the description does not explicitly state when to prefer it or mention alternatives/exclusions. It conveys the polling workflow only by implication.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_imported_datasetsList queryable official datasetsAUse this free tool when an agent needs only datasets that can be sampled or queried now. Returns fully published warehouse snapshots with dataset IDs, row counts, freshness, and provenance.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by noting these are fully published warehouse snapshots and by listing the return fields, which is valuable since no output schema is present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: the first states the use case, and the second describes the output contents. The most decision-relevant information is front-loaded and 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 zero-parameter read-only list tool, the description covers the key facts an agent needs: when to use it, what it returns, and the nature of the results. The absence of an output schema is mitigated by explicitly listing the returned fields.
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 schema already provides complete parameter coverage and no parameter documentation is needed. The description does not need to compensate for any parameter gaps, and it correctly avoids inventing unnecessary parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('List') and resource ('imported datasets') with a clear scope: only datasets that can be sampled or queried now. It also specifies the return contents (dataset IDs, row counts, freshness, provenance), which distinguishes it from sibling listing tools like list_official_sources or search_discovered_datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The opening clause 'Use this free tool when an agent needs only datasets that can be sampled or queried now' gives explicit context for when to choose this tool. It does not name alternative tools, but the 'only' qualifier helps differentiate it from broader search or source-listing siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_official_sourcesList official government sourcesAUse this free tool to inspect which federal, state, city, police, education, and other official source scopes AgentNative currently covers. Returns each source's importer, discovery state, and materialized coverage; it does not return dataset rows.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds useful behavioral context by stating the tool is free, disclosing the returned fields (importer, discovery state, materialized coverage), and explicitly excluding 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 concise sentences that front-load the tool's purpose, then provide return-value details and an explicit exclusion. Every sentence earns its place with 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 zero-parameter metadata listing tool, the description adequately covers what it lists, what each entry returns, and what it does not return. It is complete enough for an agent to invoke correctly, though it does not describe output format or pagination.
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 schema is trivially complete. Description adds no parameter details because none are needed. Baseline for zero-parameter tools 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?
States a specific verb ('inspect') and resource ('which ... official source scopes AgentNative currently covers'), and distinguishes itself from dataset-row tools by clarifying that it returns source metadata, not dataset rows. This clearly separates it from sibling tools like list_imported_datasets or query_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 clearly frames when to use the tool: to inspect current official source coverage. The explicit note that it 'does not return dataset rows' helps prevent misuse. It does not explicitly name sibling alternatives or state when not to use, 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.
query_imported_datasetQuery an imported official datasetAUse this paid read-only tool after sample_imported_dataset confirms the data is suitable. Returns up to 100 normalized rows from a published snapshot with bounded pagination, one exact-match filter, freshness, and provenance.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| skip | No | ||
| dataset_id | Yes | ||
| filter_field | No | ||
| filter_value | No |
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 context beyond annotations: the tool is paid, returns at most 100 rows, has bounded pagination (skip max 5000), supports exactly one exact-match filter, and provides normalized rows with freshness and provenance. These details help the agent predict behavior without contradiction, giving strong transparency.
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. It front-loads the usage condition ('Use after sample_imported_dataset'), then lists key functional traits. There is no redundancy or filler; every phrase contributes to the agent's understanding. Concise yet informative.
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 a decent level of complexity, the description covers the essential context: when to use it, what it returns, and its limitations (pagination, filter). It does not detail the output row schema or error behaviors, and the filter semantics are left somewhat open. However, given that the agent is expected to have interacted with sample_imported_dataset, the missing details are partially mitigated. It is strong but not exhaustive.
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 'top' and 'skip' relate to bounded pagination (max 100 and 5000), and mentions 'one exact-match filter' which maps to filter_field and filter_value. However, it does not explain what filter_field should contain (e.g., a column name) or how to obtain dataset_id, leaving significant semantic gaps. It adds moderate meaning beyond the bare schema but is not comprehensive.
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 ('imported dataset'), and key output characteristics (normalized rows, bounded pagination, one exact-match filter). It also distinguishes itself from siblings by referencing sample_imported_dataset as a precursor and implying a different purpose than aggregate_imported_dataset. The purpose is unambiguous and not a 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?
It explicitly instructs to use this tool after sample_imported_dataset confirms data suitability, providing a clear sequencing condition. It implies that this is for retrieving actual rows rather than sampling or aggregating, but does not explicitly exclude other alternatives like aggregate_imported_dataset or mention when not to use it. The guideline is present and actionable but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_us_federal_registerQuery the US Federal RegisterAUse this paid read-only tool when the free Federal Register briefing is insufficient. Returns up to 100 normalized official records filtered by date or document type, with deterministic provenance and direct source URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| type | No | ||
| end_date | No | ||
| start_date | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond annotations: paid nature, result cap of 100, normalization, filtering by date/document type, deterministic provenance, and direct source URLs. No contradiction exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tightly constructed sentences that front-load the key usage condition and then state the output characteristics. Every phrase adds useful information, with no filler or 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 description is largely complete for a read-only query tool: it covers the trigger condition, result count, filter dimensions, normalization, provenance, and URLs. However, it does not clarify whether filters are combinable or how start_date and end_date relate, and with no output schema, a bit more specificity about the record shape would improve completeness.
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 the four parameters. It mentions 'up to 100' (top), 'date' (start_date/end_date), and 'document type' (type), but it does not explain each parameter's role, relationships, or defaults. The top limit and date/type filters are only vaguely mapped, leaving significant semantics to be inferred.
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 this tool queries the US Federal Register and returns up to 100 normalized official records, with a specific verb, resource, and output scope. It also differentiates itself from the free Federal Register briefing, effectively pointing to its sibling 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?
'Use this paid read-only tool when the free Federal Register briefing is insufficient' explicitly provides a condition for selection and implies the alternative for the opposite case. This is direct guidance for when to choose this tool over the free briefing option.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_capabilityRequest a missing data capabilityAInspectUse this state-changing tool only when existing discovery and coverage tools cannot satisfy the task. Records demand for a missing dataset, aggregation, insight, filter, freshness level, or export; repeated requests increase autonomous build priority.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | ||
| capability | Yes | ||
| source_url | No | ||
| example_query | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal state-changing and non-idempotent behavior, and the description adds the non-obvious side effect that repeated requests increase autonomous build priority. It does not discuss reversibility or return behavior, but the added context is meaningful beyond the structured hints.
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 usage gate is front-loaded and the side-effect statement follows immediately. Every sentence contributes essential guidance.
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 adequate for a simple request-recording tool: it covers purpose, usage gate, and side effects, and annotations cover mutation safety. However, without an output schema or parameter-level details, an agent is left to infer response format and the role of optional parameters.
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 and four parameters, the description needed to explain the parameters. It clarifies the capability enum by listing missing capability types and mentions dataset, but it never addresses source_url or example_query, leaving a meaningful gap for optional parameters.
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 concrete resource class: missing datasets, aggregations, insights, filters, freshness levels, or exports. It also labels the tool as state-changing and contrasts it with discovery/coverage tools, making it easy to distinguish from the read-oriented siblings.
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 opening sentence provides an explicit usage gate: use only when existing discovery and coverage tools cannot satisfy the task. It does not name specific sibling tools (e.g., search_discovered_datasets or get_coverage_status), so the guidance is clear but not maximally actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_dataset_materializationMake a discovered dataset query-readyAQueue a discovered official dataset for prioritized detached ingestion. This free idempotent operation returns a durable job ID; poll get_materialization_status until it supplies sample and query URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| discovered_dataset_id | Yes | Exact catalog ID returned by search_discovered_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavior beyond annotations: it is asynchronous ('queue'), returns a durable job ID, and requires polling. It also notes the operation is 'free' and 'idempotent', matching the idempotentHint annotation. It does not contradict annotations and provides meaningful behavioral detail about the detached ingestion process.
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 that are tightly packed: the action, the idempotent/queue nature, the return value, and the polling step. No extraneous words, action is front-loaded, and the title provides a concise summary.
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 asynchronous operation with no output schema, the description adequately explains the return (durable job ID) and the next step (poll status). It doesn't cover failure modes or timeout behavior, but given the simplicity and the sibling tool for status, it 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?
The input schema fully documents the sole parameter with a regex pattern and a description referencing search_discovered_datasets. The tool description adds no new parameter semantics beyond the schema's coverage, 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?
States a specific verb ('Queue') and resource ('a discovered official dataset') and explicitly names the follow-up tool (get_materialization_status) for polling. The title reinforces the purpose. This clearly distinguishes it from search_discovered_datasets (discovery) and query/sample imported dataset 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?
Implies usage: when you have a discovered dataset ID and want to make it query-ready. It explicitly instructs to poll get_materialization_status until sample and query URLs are available, framing the expected workflow. It doesn't list explicit exclusions but the context is sufficient for an agent to route to the right tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_paid_accessCreate a Stripe access checkoutAInspectUse this state-changing tool only after free samples demonstrate value and the human owner approves payment. Creates a Stripe Checkout plus one-time claim URL; choose the $5 pass for research or $19/month subscription for recurring workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | No | pass |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool is not read-only, not idempotent, and not destructive. The description adds meaningful behavioral context: state-changing nature, Stripe side effect, one-time claim URL, and the human-approval precondition. Nothing contradicts 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?
Two sentences with no filler: the precondition is front-loaded, followed by the action and value-parameter guidance. Every clause contributes a distinct piece of 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 one-parameter mutation tool with no output schema, the description covers purpose, precondition, and option semantics well. It does not explicitly describe what the tool returns or behavior on repeated calls, leaving a small but real 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 carries the full burden for the single 'plan' parameter. It maps each enum value to its price and use case: '$5 pass for research or $19/month subscription for recurring workflows.' This is exactly the semantic information an agent needs to choose correctly.
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 — creates a Stripe Checkout plus one-time claim URL — and ties it to a payment-access context. It is clearly distinguished from sibling tools like request_capability by involving paid Stripe checkout and explicit pricing.
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 only after free samples demonstrate value and the human owner approves payment, which is strong when-to-use guidance. It does not name a non-paid alternative tool for the same capability, so it falls just short of full when-not/alternatives coverage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sample_imported_datasetSample an official dataset for freeAUse this free tool to evaluate a query-ready official dataset before paying. Returns three normalized rows, deterministic summaries, freshness, and provenance; use query_imported_dataset only after the sample proves useful.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Exact query-ready dataset ID returned by list_imported_datasets. |
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 meaningful behavioral detail beyond annotations: it promises exactly three normalized rows, deterministic summaries, freshness, and provenance, and implies the operation is free.
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 contain all key information with no filler. The free evaluation purpose is front-loaded, the return contents are efficiently listed, and the routing instruction to query_imported_dataset is included without 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?
Given one parameter, strong annotations, and no output schema, the description covers what matters: the tool's purpose, its zero-cost nature, the sample contents, and when to graduate to the paid query tool. No critical operational detail appears 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 coverage is 100% and the single parameter dataset_id is already well-described as 'Exact query-ready dataset ID returned by list_imported_datasets.' The description adds no deeper parameter semantics, but none are needed given the complete schema.
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 precise verb ('sample'), the resource ('query-ready official dataset'), and the purpose ('evaluate ... before paying'). It also lists concrete outputs and names the sibling it is not, clearly distinguishing it from query_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?
Explicitly frames this as the free pre-purchase evaluation step and instructs that query_imported_dataset should only be used after the sample proves useful. This gives unambiguous sequencing and an alternative, so an agent knows exactly when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sample_us_federal_registerGet a free Federal Register briefingAUse this free no-auth tool for current US regulatory activity or to evaluate AgentNative before paying. Returns three Federal Register records plus deterministic aggregates over the latest 25 documents, direct record URLs, provenance, and rate-limit status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations declaring readOnly, openWorld, idempotent, and non-destructive, the description discloses the exact response profile: three records, deterministic aggregates over the latest 25 documents, direct URLs, provenance, and rate-limit status. This is substantial behavior context that the annotations alone do not convey.
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 are tightly front-loaded with the key purpose and use cases, followed by a compact list of return contents. Every phrase contributes information; there is 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?
With no parameters and no output schema, the description fully covers what an agent needs to know: the target data, the evaluation context, the exact number of records, the aggregation window, and the included metadata. No critical context appears to be 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 tool has zero parameters and an empty input schema, so there is no parameter semantics burden. The description reinforces that the tool is free and requires no auth, which is sufficient for a no-parameter 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 names a specific verb and resource: 'Use this free no-auth tool for current US regulatory activity' and explains what it returns: three Federal Register records, aggregates, URLs, provenance, and rate-limit status. It also positions the tool as a sample/evaluation entry point, distinguishing it from a paid or more complete offering.
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 clearly states two contexts: monitoring current US regulatory activity and evaluating AgentNative before paying. It does not explicitly name sibling alternatives such as query_us_federal_register or state when not to use this sample, so it stops short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_discovered_datasetsSearch discovered government datasetsAUse this free tool to find datasets across all discovered official sources, including candidates not yet queryable. Returns catalog matches and materialization state; use list_imported_datasets when only query-ready data is acceptable.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum matches to return; defaults to the service limit. | |
| query | No | Optional title or description keywords. | |
| source_id | No | Optional exact official source ID returned by list_official_sources. |
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 useful context: the tool is free, covers all discovered sources, includes non-queryable candidates, and returns materialization state. This meaningfully supplements the annotations, though it does not detail rate limits or result ordering.
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 wasted words. The main purpose is front-loaded, followed by return behavior and a clear routing instruction. Every clause contributes 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 simple optional-parameter search tool with rich annotations and full schema coverage, the description is largely sufficient. It names the key return elements ('catalog matches and materialization state'), though the absence of an output schema means 'materialization state' is left somewhat undefined.
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% parameter coverage, including descriptions for limit, query, and source_id. The description adds no extra parameter-level semantics, but the schema already carries the full burden, so a baseline score 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 ('find'), a specific resource ('datasets across all discovered official sources'), and explicitly scopes the tool to include candidates not yet queryable. It also distinguishes itself from list_imported_datasets, clarifying what this tool is and is not.
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 when-to-use guidance: use this when exploring all discovered sources, including non-queryable candidates, and use list_imported_datasets when only query-ready data is acceptable. This directly helps an agent select between siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_public_datasetsSearch public datasetsAUse this free tool first when an agent needs official US federal data but does not yet know the dataset ID. Searches normalized catalog metadata and returns matching datasets with provenance; it does not query dataset rows.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Plain-language topic, agency, or dataset keywords, such as employment, schools, or air quality. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context beyond annotations: it is free, searches normalized catalog metadata, returns provenance, and does not query 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?
The description is two sentences with no filler. The usage directive is front-loaded, followed by the operational scope and a key negative constraint. Every sentence 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 search tool, the description covers the trigger condition, the data scope, the operational behavior, and the high-level return value. It lacks explicit response format or pagination details, but the tool is simple enough that this 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?
Schema coverage is 100% and the query parameter already has a meaningful description with examples. The tool description adds no parameter-specific detail beyond what the schema provides, 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 states a specific verb ('Searches') and resource ('normalized catalog metadata'), and clarifies it returns matching datasets with provenance. It also differentiates itself from row-level query tools by explicitly stating 'it does not query 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 explicit guidance: 'Use this free tool first when an agent needs official US federal data but does not yet know the dataset ID.' This is a clear context for use, though it does not name or exclude specific sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Normalized official data with provenance, aggregations, insights, free samples and agent access.
Normalized official data with provenance, aggregations, insights, free samples and agent access.
US public-records intelligence for AI agents — companies, SEC, courts, spending, licenses.
US healthcare data for AI agents: CMS, FDA adverse events, CDC, NPPES NPI. Keyless, real samples.
