osf-data-marketplace

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

With no annotations, the description carries full burden. It discloses that the tool performs a live lookup, returns specific fields (CRD number, registration status, disclosure events, permanent bar, current employers, FINRA URL), and handles payment automatically. It does not mention rate limits or authentication, but covers key behavioral traits.

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 paragraph that is information-dense without wasted words. The first sentence immediately states the main purpose, and every subsequent sentence 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?

Given one parameter, no output schema, and no annotations, the description covers the tool's purpose, behavior, return values, use cases, and payment method. It feels sufficiently complete for the tool's complexity.

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 single parameter 'query' is implicitly described as the name of the broker or firm, but the description lacks explicit format or examples. Schema coverage is 0%, so the description should compensate more, but it does add meaning by indicating what the query represents.

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 resource (stockbroker, investment adviser, brokerage firm) and the action (check disciplinary history). It distinguishes from sibling tools by specifying FINRA BrokerCheck registry and mentioning it's a companion to the authority sanctions screen.

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 explicit use cases: KYC, AML, counterparty due diligence, adviser vetting, and investment fraud checks. It also notes the payment mechanism, but does not explicitly state when not to use the tool or alternative 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?

No annotations are provided, so the description bears full responsibility. It discloses that the tool is FREE, returns a specific set of fields, and aggregates from many sources with provenance URLs. It does not mention rate limits, authentication, or pagination behavior, but the output schema exists to cover return format.

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 well-structured with the main purpose front-loaded, followed by filter options and a comprehensive list of data verticals. The list of verticals is verbose but adds value. Every sentence contributes, though it could be slightly more concise.

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 the existence of an output schema, the description adequately covers the tool's purpose, filters, and its relationship to get_record. It provides sufficient context for an agent to use the tool effectively, though pagination parameters (limit, offset) are not discussed.

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%, so the description must compensate. It explains 'source' and 'data_type' with examples, but 'limit', 'offset', and 'record_key' are not mentioned in the description. Thus, 2 of 5 parameters are clarified, partially compensating but not fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Browse the OSF catalog (FREE)' and lists the outputs (record_ids, prices, data types, provenance URLs). It distinguishes from siblings by implying this is a browsing/pre-purchase tool, while get_record is for purchasing. The verb 'browse' and resource 'catalog' are specific.

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 explains optional filters (source, data_type) and advises to call get_record to purchase selected records, providing clear context. However, it does not explicitly state when not to use this tool or list alternative tools for similar tasks.

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?

With no annotations, the description discloses paid nature (x402 USDC), return content (full record + provenance), and lists example data sources. Lacks details on rate limits or auth beyond x402, but provides good behavioral context.

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 front-loaded with the core action, followed by context about OSF verticals and usage hints. Slightly verbose with the long list of sources, but 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?

Comprehensive for a simple retrieval tool: explains payment, return value, data sources, and how to find inputs. No output schema exists, so the description ably fills the gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter (record_id) is described as an identifier, and the description explains how to obtain valid IDs (via get_catalog). This adds meaning beyond the bare schema, which has 0% coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool purchases and retrieves one verified OSF record by record_id, including provenance. It distinguishes from siblings like get_catalog (browsing) and the many search 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?

Explicitly advises browsing get_catalog first to find record_ids and prices, establishing clear usage context. Does not explicitly exclude other scenarios, but the context is clear.

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

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

With no annotations, the description fully discloses behavior: it's a paid tool ($0.05, x402 USDC on Base), returns KEV status, EPSS score, CVSS severity, and provenance URLs for verification. It also notes automatic payment handling. This is highly transparent.

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 three sentences, each serving a purpose: function, return details and use cases, and payment info. No unnecessary words, front-loaded with the core action.

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 no output schema, the description explains the returned fields (KEV, EPSS, CVSS, URLs). It covers payment and input format. It could mention error handling for invalid CVE IDs, but overall it's complete for a simple check 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 only parameter (cve_id) has no schema description (0% coverage). The description adds an example format 'CVE-2026-33017', providing guidance beyond the schema. This compensates for the schema 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 clearly states the tool's purpose: 'Check whether a specific CVE is being actively exploited in the wild'. It specifies the resource (CVE) and action (check). Among siblings, which are mostly search tools for different domains, this tool is distinct and unique.

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 context for usage: 'For vulnerability management, patch prioritization, threat intelligence, and DevSecOps agent workflows.' While it does not explicitly exclude other uses or name alternatives, the purpose is sufficiently clear to guide selection.

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?

With no annotations, the description fully discloses behavior: payment cost ($0.05), automatic x402 payment, resolution against public registries, and return fields (legal name, status, type, jurisdiction, identifiers, provenance URL). No destructive actions or rate limits mentioned, but these are not needed for a read-only lookup.

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 informative and front-loaded but slightly long due to the list of registries and use cases. Every sentence adds value; no waste.

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 the complexity (multiple registries, payment) and lack of output schema, the description is remarkably complete. It explains inputs, returns, payment, and use cases, leaving no major gaps for an AI agent.

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%, but the description compensates by explaining how to use the single parameter 'query': 'Pass an identifier (NPI, LEI, FDIC cert, CIK, or EIN) for an exact match, or a name for candidate matches.' This adds critical meaning beyond the schema's 'string' field.

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 verb 'verify' and the resource 'entity/counterparty'. It lists specific registries (CMS NPI, GLEIF LEI, etc.) and differentiates between identifier (exact match) and name (candidate matches), making it distinct from sibling tools like screen_entity.

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 specifies when to use the tool: 'For KYC, KYB, counterparty due-diligence, provider verification, nonprofit vetting, and onboarding agent workflows.' It also explains the payment model. However, it does not explicitly state when not to use it or mention alternatives.

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?

With no annotations provided, the description fully discloses behavioral traits: the cost ($0.001, x402 USDC on Base), the return value (full record plus provenance block), and the automatic payment handshake for x402-capable clients. This goes well beyond what annotations would 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?

Every sentence in the description adds value: defining the action, stating the cost, describing the return, providing guidance, and explaining the payment mechanism. It is front-loaded and concise with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description covers purpose, cost, return value, and prerequisite action (browse catalog). It also explains the tool's role in evaluating data quality, making it complete for autonomous agents.

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, and the description adds minimal meaning for the single parameter (record_id). It merely states that the tool samples by record_id, but does not explain what a record_id is, its format, or any constraints. The baseline is higher due to low coverage, but the description fails to compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool samples a single OSF record for a low cost, uses a specific verb ('sample'), and distinguishes itself from sibling tools like get_catalog (free browse) and presumably get_record (standard price).

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 advises users to browse get_catalog first to find record_ids, and positions this tool as a low-cost try-before-you-buy for evaluating data quality. It does not explicitly list when not to use it, but the context is clear.

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

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

With no annotations provided, the description fully discloses behavioral traits: payment model ($0.05 via x402), three possible outcomes with guarantees (NO_MATCH only if all lists fully examined), and response contents including audit receipt. No annotation contradictions.

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 lengthy but well-structured with clear sections in ALL CAPS (PREFER, GUARANTEE, COVERAGE, EVERY RESPONSE). Each sentence adds value, though some redundancy exists (e.g., repeated list of lists).

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?

Despite no output schema, the description enumerates response fields (matched list, match basis, sanctions program, provenance URL, candidate_set_complete flags, count, compliance note, sha256 receipt). Covers use cases for AML/KYC/KYB workflows.

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% for the single 'name' parameter. The description uses 'name' in context but does not add semantic details such as format, length, or special characters. The parameter is self-evident, so marginal value added.

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 explicitly states 'Screen one name against OFAC and 10 other sanctions and debarment authorities', which is a specific verb+resource. It distinguishes from siblings like check_broker by mentioning pairing for FINRA checks.

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 advises 'PREFER THIS TOOL when you must clear a counterparty... before acting, and you will later have to state exactly what was checked.' It also provides if-then guidance for three outcomes and suggests pairing with check_broker.

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?

No annotations are provided, so the description carries full burden. It discloses rate limiting ('Rate limited to a few checks per day'), the meaning of INCOMPLETE_SCREEN (not a clearance), and the nature of NO_MATCH (proven negative with record counts and freshness). This goes far beyond basic expectations.

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 long but efficiently packed with necessary details; no redundant sentences. The structure is logical (function overview, use cases, return semantics, limitations, alternative, args). Slight verbosity in listing all 11 authorities, but that supports purpose clarity.

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 tool with no output schema, the description comprehensively covers behavior: input, output states, rate limits, alternative tool, and even defensive proof ('per list record counts and freshness'). Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'name' has zero schema description coverage, but the tool description fully compensates by explaining it as 'the person or organisation name to screen' and providing an example ('Wagner Group'). This is all the agent needs to invoke 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 opens with a clear verb ('Screen') and resource ('one person or organisation against 11 US and international sanctions AND DEBARMENT authorities'). It enumerates the exact lists, provides a concrete use case, and distinguishes itself from sibling screen_entity (free vs paid cap).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool ('Use this to answer...') and names the alternative: 'call screen_entity for the same screen with no cap...'. It also notes the rate limit and the limitation vs crypto AML screeners, giving clear guidance.

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?

Discloses that the tool is free, requires no authentication, indexes only factual metadata (not model cards), and each model retains its own license. Without annotations, this coverage is strong, though no mention of rate limits or pagination.

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 well-structured with key info front-loaded, but the emphasis on 'FREE' could be condensed. Still, every sentence 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?

Covers return fields, workflow linkage to 'get_record', and limitations. Missing details on pagination or result limits, but overall complete for a search 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?

With 0% schema coverage, the description adds examples for the 'query' parameter (e.g., 'image segmentation', 'nvidia'), providing practical guidance beyond the schema's type definition.

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 it searches AI/ML models on Hugging Face Hub by various criteria, with a specific verb and resource. It distinguishes from sibling search tools by its domain focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases (model selection, license screening, dependency review) and mentions the complementary tool 'get_record'. Does not explicitly exclude other sibling tools, but context is clear.

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

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

With no annotations provided, the description carries the full burden of disclosure. It explicitly discloses it is FREE with no payment/key, explains it returns a 'coverage block' about upstream file availability, and clarifies the paywall boundary (free search vs paid full record via get_record). It doesn't detail rate limits or latency, but the core behavioral contract (free, results format, paywall split) is well covered.

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 information-dense with every section contributing value: what's searched, what's returned, what's free vs paid, and usage examples. Slightly long but each sentence earns its place. The Args section is front-loaded with practical examples that immediately help a user.

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 a clear return contract (lists returned fields + coverage block), no output schema, and no annotations, the description is remarkably complete. It covers inputs, outputs, the free/paid boundary, use cases, and the upstream data-source caveat. It addresses complexity well despite the lean 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 0% and there's only one parameter (query). The description adds substantial value by giving concrete query examples including tail numbers, manufacturer names, owner names, and location strings like 'MARION TX'. This compensates well for the sparse schema, though since it's a single free-text parameter, there's limited additional semantics to add.

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 is very specific: 'Search US civil aircraft registrations from the FAA Releasable Aircraft Database' with explicit examples of searchable fields (manufacturer, model, tail number, owner, etc.). It clearly distinguishes from siblings by being domain-specific (aircraft registry) and naming get_record as the related purchase tool.

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 (free search of aircraft records) and when to use the alternative: 'call get_record with a record_id to buy the full registration.' It also lists concrete use cases (aircraft ownership lookup, fleet research, asset tracing, due diligence) and provides search examples in the Args section.

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?

No annotations exist, so the description bears the full burden. It explicitly states the tool is FREE, no payment/no key. It describes what fields are returned: title, agency, dates, URL, record_id. It acknowledges the separate paid record retrieval. It does not mention rate limits or error behavior, but for a simple search tool this is adequate.

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 front-loaded with the most critical detail (free, no key). The body is structured: sources, returns, use cases, then the Args section. Minor redundancy ('FREE, no payment and no key' repeats no cost), but overall efficient.

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 the tool's complexity (multiple databases), single parameter, and no output schema, the description adequately covers purpose, agencies, return fields, and pricing. It lacks details on result pagination or error states, but these are not critical for basic usage.

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% coverage (no description for query). The description adds 'keyword(s) to search' in the Args section, which clarifies the parameter's purpose. However, it does not provide format examples, length limits, or boolean logic hints, leaving the agent to infer usage.

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 specific verbs and resources: 'searches recalls and consumer complaints' across multiple agencies (CFPB, NHTSA, CPSC, FDA). It clearly distinguishes itself from sibling tools like get_record and screen_entity by explaining what it returns (summary) and that a separate paid call provides full records.

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 states use cases: 'product safety review, supplier screening, and recall monitoring'. It implies when to use this tool vs. get_record (free summary vs. paid full record). However, it does not explicitly exclude when not to use it or compare to other search siblings (e.g., search_cyber_threats).

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?

Despite no annotations, description discloses it is free, no key required, which sources are searched, and what fields are returned. It also mentions that get_record is a paid call. No contradictions. Lacks advanced details like rate limits but sufficient given tool type.

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?

Description is information-dense but reasonably front-loaded with key traits (free, no key). It lists multiple data sources and return fields efficiently. Minor redundancy (e.g., 'FREE, no payment and no key' is a bit repetitive) but not excessive.

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 no output schema, the description explains return fields (id, title, severity, provenance URL, record_id). It also explains the workflow (search free, get_record paid). Lacks pagination or ordering details but covers essential context for a search 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?

Single parameter query is described with examples ('e.g. Microsoft Exchange') and clarifying it is for keyword search. Schema coverage is 0% but the description compensates by providing usage context and example, making the parameter's purpose clear.

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 searches cybersecurity intelligence across multiple sources (CVE, EPSS, CISA KEV, etc.) and specifies return fields. It distinguishes itself from sibling is_cve_exploited by noting when to use that alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: if user knows CVE id and only wants exploit status, call is_cve_exploited instead. Also notes that get_record is needed for full record. This helps agent choose 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?

No annotations are provided, so the description carries the full burden. It mentions that the tool is free and requires no key, which is a positive cost trait. However, it does not disclose rate limits, pagination behavior, or whether the operation is read-only. The description is somewhat transparent but lacks details about potential limitations or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, starting with a key selling point (FREE, no payment), then stating the purpose, listing data sources, describing return fields, and providing workflow and usage context. No sentence is wasted, and the 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?

Given no output schema, the description adequately explains the returned fields (series title, issuing agency, period, provenance URL, record_id) and the workflow (search then get_record for full data). It covers the tool's functionality and data sources well, but could mention limits on result count or error handling for a complete picture.

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 single parameter 'query' is described as 'keyword(s) to search', which adds minimal meaning beyond the schema. With 0% schema description coverage, the description must compensate, but it only adds a brief note about keyword(s). It does not specify format, examples, or how multiple keywords should be structured.

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: searching for official economic indicators from a wide range of agencies. It specifies the verb 'Searches' and the resource 'economic indicators', and distinguishes itself from sibling tools like get_record and other search tools by focusing on economic data.

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 usage context such as 'For macro research, model inputs, and grounding an answer in a citable official series'. It also implies when not to use: when you need the full series data, use get_record instead. It could be more explicit about alternatives among siblings, but overall it gives clear guidance.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool is free and returns record metadata (title, agency, dates, URL, ID). However, it omits behavioral details such as rate limits, data freshness, authentication requirements, or whether the search is real-time. This is adequate for a simple search tool but lacks completeness.

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 well-structured: it leads with the key selling point ('FREE, no payment and no key'), then explains scope, return fields, and use cases in a few sentences. The Args section is appended neatly. Though somewhat lengthy, every sentence 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?

Given the single parameter and no output schema, the description provides a thorough overview: data sources, returned fields, use cases, and the relationship to get_record. It does not mention pagination, result limits, or geographical coverage limitations, but covers the essential context for an agent to decide to use this 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 must compensate. The description includes 'Args: query: keyword(s) to search,' which explains the only parameter. However, it lacks details like query syntax (e.g., boolean operators, exact match) or examples, providing only basic semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches environmental, hazard, and earth science records for a given place or event, listing specific agencies like USGS, NOAA, FEMA, EPA, and GBIF. It distinguishes from siblings by focusing on environmental data, but does not explicitly differentiate from other search tools like search_consumer_protection.

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 the tool is free with no payment or key, and directs users to call get_record to purchase full records. It suggests use cases like site diligence and disaster response. However, it does not specify when to avoid using this tool (e.g., for non-environmental queries).

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?

No annotations are present, so the description carries full burden. It does not disclose behavioral traits such as rate limits, pagination, error handling, data freshness, or whether the search is case-sensitive. Only describes what it returns, not how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loads critical information ('FREE, no payment and no key'), and efficiently covers purpose, returns, and usage guidance in a few sentences without unnecessary detail.

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 the tool has one parameter and no output schema, the description covers the essential information: data sources, returned fields, and follow-up tool (get_record). Minor gaps like pagination or result limits are acceptable for a simple search tool, but completeness is high.

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%, but the description provides a meaningful explanation of the single parameter 'query' with an example ('navy radar'). It adds value beyond the schema by explaining the parameter's purpose. Could be improved with constraints but sufficient for a simple string parameter.

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 searches US federal spending across three specific sources (USAspending, SAM.gov, Grants.gov) and returns specific fields. It distinguishes itself from sibling tools like get_record by explaining the workflow (search first, then buy full record).

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 tells when to use: 'Use this to find out whether OSF holds what you need before spending anything, then call get_record...'. Also highlights that it's free with no key needed, and mentions specific use cases like govcon market intelligence and competitor tracking.

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?

With no annotations, the description discloses free access, no key required, return fields (title, identifiers, URL, record_id), and hints at pricing for full record via get_record. Lacks details on rate limits or pagination, but sufficiently covers key behavioral traits.

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 detailed but well-structured, starting with key features (free, no key). A few redundant phrases, but overall efficiently conveys necessary 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 search tool with no output schema or annotations, the description covers purpose, data sources, return fields, pricing model, and alternative tools. It is comprehensive for the given complexity.

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 has 0% coverage (no description for 'query' param). Description compensates by explaining query is keywords and gives examples (provider name, NPI, drug name, etc.), adding meaningful context beyond the 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 clearly states it searches healthcare databases (CMS NPPES, RxNorm, ClinicalTrials.gov, FDA recalls) and returns specific fields. It also distinguishes from sibling 'lookup_entity' by specifying when to use that instead.

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 lists use cases: provider verification, prescriber vetting, trial status checks, drug safety review. Provides alternative tool 'lookup_entity' for verifying by identifier.

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?

No annotations provided, so description carries full burden. It discloses free access, no key required, returns specific fields, and that get_record is needed for full opinion. Lacks mention of result limits or pagination, but is otherwise transparent.

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?

Description is concise and well-structured: opens with free access, then scope, returns, sibling distinction, and parameter with examples. No redundant sentences; each 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?

Given a simple search tool with one param and no output schema, description covers data sources, returned fields, and how to get full record. Missing details on result count limits, ordering, or error handling, but overall sufficient.

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%, but description compensates by defining 'query' as keyword search and providing two clear examples. Could elaborate on advanced syntax (e.g., operators, boolean), but sufficient for basic use.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool searches US federal court opinions and SEC litigation releases, naming specific sources (SCOTUS, appellate circuits) and return fields. It explicitly distinguishes from sibling tool search_regulations_law.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides usage context: for precedent checks, citation lookup, enforcement history. Notes that regulations are separate. However, does not explicitly state when not to use or list alternative search tools among the many 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?

Discloses that the tool is free and requires no authentication. Describes returned fields (e.g., patent number, title, assignee). Lacks detail on pagination or rate limits, but overall informative for a read-only search.

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?

Well-structured with front-loaded purpose and returns. Slightly verbose but every sentence adds value; no 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?

Given a single-parameter search tool with no output schema, the description fully explains input, output, and usage context, including a pointer to get_record for full records.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'query' is explained with concrete examples (e.g., 'lithium battery cathode', 'Panasonic', '12678711') and the description lists valid query types (title, assignee, inventor, etc.), adding significant meaning beyond the 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 clearly states it searches granted US patents and lists searchable fields, effectively distinguishing it from sibling tools like search_sec_filings or search_legal_cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases (prior art checks, freedom to operate, competitor IP monitoring) and tells when to use the sibling get_record to purchase full records.

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?

With no annotations, the description carries full burden. It discloses that the tool is free but returns only metadata; full documents require calling get_record with a record_id (implying a paywall). It does not mention rate limits or authentication, but for a search tool, the key behaviors are covered.

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 front-loaded with key value ('FREE, no payment and no key'), followed by sources, returns, and usage notes. It's well-structured but slightly verbose (e.g., repeating 'FREE'). Still, every sentence 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?

Given the tool's complexity (searching multiple sources, paywall for full docs), the description is comprehensive: covers scope, return fields, intended use, and how to get full documents via sibling tool. No output schema, but returns are described explicitly.

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%, so the description must explain the lone parameter. It says 'query: keyword(s) to search.' This adds basic meaning but lacks details like syntax, boolean operators, or example formats. Baseline 3 is appropriate given single parameter and minimal added value.

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 searches US federal regulations and related documents, lists specific sources (eCRF, Federal Register, etc.), and explicitly distinguishes from sibling tool search_legal_cases for case law. Answers the question 'what does US federal regulation say about this, and what is being changed'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases: 'For regulatory compliance, policy monitoring, and rulemaking research.' Clearly states when not to use: 'Court opinions are a separate tool: call search_legal_cases for case law.' Also mentions it's free with no key, giving clear guidance on prerequisites.

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?

No annotations provided, so description carries full burden. It discloses free access, no key needed, returns specific fields, and mentions the option to buy full records. It does not discuss rate limits or error handling, but the transparency is adequate.

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 front-loaded with key info (FREE, coverage, returns) and ends with usage guidance. It is slightly wordy but every sentence adds value. Could be trimmed slightly for conciseness.

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, no output schema, and no annotations, the description is quite complete. It explains tool purpose, return fields, and relationship to get_record. It provides enough context for an agent to decide whether and how to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so description must add meaning. It describes the 'query' parameter as 'keyword(s) to search' and provides an example ('CRISPR off-target'). This adds clarity beyond the schema's type-only definition.

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 it searches scholarly works across multiple databases (arXiv, CrossRef, etc.), returns metadata (title, authors, venue, etc.), and is free. It distinguishes itself from the sibling tool get_record by mentioning that get_record retrieves full records.

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 it's free to search and advises to 'probe coverage before you spend', then call get_record to buy full records. It suggests use cases: literature review, citation checking, research agent workflows. However, it does not list when not to use it.

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

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

The description discloses that the tool is free, returns metadata (filer, form type, date, URL, record_id), and that full filings require payment via get_record. With no annotations, this adequately informs the agent of behavioral traits.

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 well-structured, starting with key facts (FREE, no key), then listing searchable content, return fields, use cases, and parameter details. It is concise for the information provided, though slightly wordy.

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 the tool has one simple parameter and no output schema, the description fully explains what is returned and how it relates to the get_record tool. No additional details are needed for an agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds an example ('e.g. 'Berkshire 13F'') and clarifies that query is keywords. However, schema coverage is 0%, and the description does not fully specify query syntax, formatting, or operators, offering only basic guidance.

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 'Search US SEC filings' with a specific verb and resource, and lists many filing types. It distinguishes itself from sibling search tools (e.g., search_patents, search_legal_cases) by focusing on SEC filings.

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 specifies use cases: 'For financial research, insider activity monitoring, and issuer due diligence.' It also notes that full filings require a separate tool (get_record). However, it does not explicitly state when not to use this tool.

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