osf-data-marketplace

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

With no annotations, the description carries the full burden. It discloses that the lookup is live against FINRA, mentions automatic payment via x402, and lists the specific return fields. However, it does not mention error handling, rate limits, or authentication beyond payment.

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 main purpose, followed by source, return fields, use cases, and payment. Every sentence adds value, with no wasted words. It is concise yet detailed.

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 and no output schema, the description covers purpose, use cases, return values, and payment. It lacks explicit input format guidance and error behavior, but overall provides sufficient context for agent invocation.

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

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

The input schema has one parameter 'query' (string) with 0% coverage. The description implies the query is a name or identifier (e.g., stockbroker, firm) but does not explicitly state the expected format (e.g., CRD number vs name). It adds some meaning but not full clarity.

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 checks a stockbroker, investment adviser, or brokerage firm for disciplinary history, specifying the FINRA BrokerCheck registry. The verb 'check' paired with the resource 'broker' is specific and distinguishes it from sibling 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?

The description explicitly lists use cases: KYC, AML, due diligence, vetting, fraud checks, and mentions it as a companion to another sanctions screen. It provides clear context for when to use but does not explicitly state when not to use or 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool is FREE, returns specific fields, and that records include provenance URLs. It implies read-only behavior but does not explicitly state it. Pagination behavior (limit/offset) is mentioned only in the schema, not in the description. A score of 3 reflects that the description provides moderate behavioral context but falls short of being comprehensive.

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 the core purpose and return value, then filters, then a broad context paragraph. It is front-loaded and informative without being overly verbose. The second paragraph is dense but valuable for understanding the tool's domain. Minor excess could be trimmed, but overall 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?

An output schema exists (as per context signals), so the description need not detail return values. It explains the tool's purpose, filter options, and how the catalog integrates with other tools (get_record). It also provides extensive domain context about the data sources. Missing explicit guidance on pagination, but that is handled by the schema. Overall, it is sufficiently complete for a browsing 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. It explains the source and data_type parameters with examples, adding meaning beyond the schema. However, it does not describe limit, offset, or record_key parameters. Thus, it partially covers parameter semantics, justifying a score of 3.

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: 'Browse the OSF catalog (FREE).' It specifies the verb (browse), the resource (OSF catalog), and the return values (record_ids, prices, data types, provenance URLs). It distinguishes from siblings by mentioning that get_record is used for purchase, showing an understanding of tool differentiation.

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

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

The description tells when to use this tool (browsing the catalog) and indicates filtering options. It also references the sibling tool get_record for purchasing, providing context on when not to use this tool. However, it does not explicitly state when to avoid this tool or list alternatives for specific filters.

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 involves a purchase (payment) and returns the record with provenance. No annotations provided, so description fully covers behavioral traits 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?

Three sentences, front-loaded with core purpose. The third sentence on OSF verticals, while informative, is slightly tangential 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?

Covers purpose, parameter source, return contents (record + provenance), and payment handling. Lacks error handling details but is largely complete for a simple retrieval 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 coverage is 0%, but description explains record_id comes from get_catalog and is used to identify the record, adding critical meaning beyond the schema's minimal label.

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 retrieves a specific OSF record by record_id, with payment. It distinguishes from sibling get_catalog (browsing) by specifying purchase and retrieval.

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 to use get_catalog first to find record_ids and prices, and mentions automatic payment via x402-capable MCP clients. Provides clear when-to-use context.

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 payment requirement and data sources with provenance URLs, but does not explicitly state it's read-only or mention potential error cases.

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 4 sentences, front-loaded with purpose and key details. Slightly verbose on payment but overall efficient. 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?

Despite lacking output schema, description explains all expected return elements (status, scores, provenance URLs). Covers payment and use cases. Could mention behavior for invalid CVE IDs but otherwise 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 only parameter (cve_id) has no schema description (0% coverage), but the description provides a concrete example and format. This adds sufficient meaning for the agent to use it 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 clearly states the tool checks if a CVE is exploited, with specific outputs (CISA KEV, EPSS, CVSS) and example input. It distinguishes from sibling tools which cover broader threat searches.

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 (vulnerability management, patch prioritization, etc.) and mentions payment process. However, it doesn't explicitly state when not to use or compare to 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?

No annotations exist, so the description carries full burden. It discloses payment handling (x402 automatic handshake), return fields (legal name, status, type, jurisdiction, identifiers, provenance URL), and the operational behavior (query against multiple registries). It lacks details on error handling or rate limits, but covers essential 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 a single coherent paragraph (105 words). It front-loads the purpose and then adds details (registries, parameter usage, returns, payment). It is not overly verbose, but could benefit from bullet points for the registries or structured sections. Still, it is clear and 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?

For a tool with one parameter, no output schema, and no annotations, the description covers: purpose, registries, parameter usage, return fields, use cases, and payment. It is mostly complete, but lacks details on result limits, error responses, or rate limits. Adequate for effective use.

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 one parameter 'query' with 0% schema description coverage. The description compensates fully: explains that query can be an identifier (NPI, LEI, FDIC cert, CIK) for exact match or a name for candidates. This adds critical meaning beyond the bare schema, earning a 5.

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 verb ('verify'/'lookup'), resource ('entity/counterparty'), and distinguishes from siblings by specifying the registries queried (CMS NPI, GLEIF LEI, FDIC, SEC EDGAR CIK). It provides a specific action and scope, earning a 5.

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

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

The description explains when to use: pass an identifier for exact match or name for candidates. It lists use cases (KYC, KYB, due-diligence, etc.). However, it does not explicitly state when not to use or compare to siblings like screen_entity or search_sec_filings, missing the top score.

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 the full burden. It discloses the paid nature ($0.001, USDC on Base, x402 handshake), the output content (full record + provenance), and the return type. It could add details about rate limits or idempotency, but the essential behavioral traits 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 information-dense but slightly verbose for its length. It front-loads the core purpose and lists key details (price, payment, prerequisites, output). Every sentence adds value, though it could be more concise without losing 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?

Given a single-parameter tool with no output schema, the description covers the input source (get_catalog), cost, payment mechanism, and output (full record + provenance). It also explains the use case, making the tool's purpose and operation fully understandable.

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 that record_id comes from get_catalog and that it samples a verified OSF record. While it doesn't specify the integer's format or constraints, it provides meaningful context beyond the schema's bare type.

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 samples one verified OSF record by record_id for a fee, returning the full record and provenance block. It distinguishes from siblings by mentioning 'Browse get_catalog first (free) to find record_ids' and positioning itself as a try-before-you-buy option, implying a different use case than get_record or 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?

It explicitly says when to use (low-cost evaluation before buying) and suggests a prerequisite ('Browse get_catalog first'). It does not discuss when not to use or explicitly name alternatives, but the context is clear enough for an agent to decide.

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. It discloses that the tool is paid ($0.08 via x402), returns match/no-match with details, and includes an audit receipt. It does not mention rate limits or failure modes, but the disclosure is good.

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 relatively long but well-structured and front-loaded with purpose. Every sentence adds value, covering use cases, output, and payment. Minor redundancy could be trimmed.

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 simplicity (one parameter, no output schema, no annotations), the description is quite complete. It explains what lists are checked, what is returned (match basis, provenance URL, compliance note, receipt), and the payment method. It does not cover error scenarios or limits.

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 has only one parameter 'name' with no description, so coverage is 0%. The description adds meaning by stating it screens a person or entity name against sanctions, but does not specify formatting or constraints, leaving some ambiguity.

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 screens entities against international sanctions lists, specifying 9 authorities and 120,000+ parties. It distinguishes itself from sibling tools like lookup_entity or search_regulations_law by focusing on sanctions screening for AML/KYC workflows.

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 mentions 'For AML, KYC, watchlist, OFAC, and sanctions-compliance agent workflows,' providing clear context for when to use. However, it does not explicitly state when not to use it or suggest 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?

Discloses that it indexes factual repository metadata only, does not reproduce model cards. No annotations exist, so description carries burden. No mention of rate limits or auth, but appropriate for a search tool.

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

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

Two paragraphs plus Args section, front-loaded with purpose. Efficiently conveys key information without extraneous 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?

Covers purpose, return fields, and limitations adequately for a single-parameter search tool. No output schema, but returns are described inline.

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?

Only parameter is query with 0% schema coverage, but description provides helpful examples ('image segmentation', 'nvidia') beyond the schema's minimal title.

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 for AI models on Hugging Face Hub by various criteria (name, author, task, etc.), distinguishing it from sibling tools like search_patents or search_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?

Explicitly recommends get_record for full metadata and notes limitations (no model card text, license retained). Could specify when not to use but provides solid context.

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?

Given no annotations, description fills the gap by listing returned fields (tail number, registrant, location, etc.) and implying read-only nature. No side effects or rate limits mentioned, but search tools typically have minimal behavioral concerns.

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 plus a clear Args section. Every sentence adds value; no wasted words. Front-loaded with purpose and searchable fields.

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?

Complete for a single-parameter search tool. Describes input, output fields, and relationship to sibling. No output schema needed given full coverage of return values.

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 examples (tail number 'N17973', owner name, 'MARION TX'), adding significant meaning beyond the bare schema type definition. Schema coverage is 0%, so description fully compensates.

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?

Clearly states the verb (search), resource (US civil aircraft registrations), and multiple search criteria (tail number, owner, city, etc.). Distinguishes from sibling tools like get_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 states use cases (ownership lookup, fleet research, asset tracing) and suggests get_record for full records. Lacks specific when-not-to-use guidance but provides clear context.

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 that the tool returns record_ids that can be purchased, which is a key behavioral trait. It also notes the full history since 2004. However, it does not mention rate limits, authentication needs, or behavior on empty results.

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 plus a minimal Args section. It is front-loaded with the main purpose. The Args listing essentially repeats the schema, but the overall length is 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 simplicity (one param, no annotations, no output schema), the description covers purpose and return type. However, it lacks details on result count limits, ordering, or any prerequisites. For a search tool in a group of siblings, more contextual guidance would be beneficial.

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?

Only one parameter (query) with 0% schema description coverage. The description adds 'keyword(s) to search,' which clarifies usage but is minimal. Baseline 3 is appropriate given the low coverage and the description's slight compensation.

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 consumer protection data, listing specific agencies (CFPB, NHTSA, CPSC, FDA) and specifying full history since 2004. It distinguishes itself from sibling search tools by focusing on this domain.

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

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

The description implies it is used for consumer protection searches but provides no explicit guidance on when to use it versus sibling tools like search_cyber_threats or search_healthcare. No exclusions or alternatives are mentioned.

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 the full burden. It discloses the return fields (id, title, severity/score, provenance URL, record_id) and implies read-only behavior as a search operation. It doesn't mention rate limits or pagination, which is acceptable for a simple search tool.

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

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

The description is well-structured, front-loading the key sources and return fields, followed by usage guidance and parameter details. It is informative without being overly verbose, though it could be slightly more concise by removing minor redundancies.

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 adequately covers the core functionality: what data sources are searched, what fields are returned, and how to get full records. It lacks details on pagination or sorting, but for a search tool this is 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?

The description includes an 'Args' section that clarifies the 'query' parameter with an example ('Microsoft Exchange'). Although schema description coverage is 0%, the description adds meaningful semantic context beyond the schema's property name.

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 the tool searches across multiple cybersecurity intelligence sources (CVE, EPSS, CISA KEV, etc.) and lists return fields. It clearly distinguishes from sibling tools like get_record (for full records) and other search tools covering different domains.

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 when to use this tool (searching threat intel) and mentions an alternative (get_record for full details). However, it doesn't explicitly exclude usage when other siblings like is_cve_exploited might be more appropriate, though the context from sibling names provides some 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; the description mentions returns record_ids but does not disclose other behavioral traits such as authentication, rate limits, 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?

Two sentences plus an Args line, zero wasted words, and front-loaded with key action and sources.

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 search tool with one parameter and no output schema, the description adequately covers what it searches and what it returns (record_ids), though it omits pagination or limits.

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 'keyword(s) to search' to the query parameter, providing meaning beyond the schema's type and title, compensating for low schema coverage.

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

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

The description clearly states the verb 'Search' and resource 'economic indicators', listing specific sources (FRED, World Bank, etc.) to distinguish it from sibling 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?

The description implies usage for economic data from listed sources but provides no explicit guidance on when to use versus alternatives or when not to use.

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 'returns record_ids to buy' implying a transactional nature, but lacks details on rate limits, authentication, or potential delays.

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 short and front-loaded with purpose, but includes an 'Args:' section that redundantly restates the schema, making it slightly less 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 complexity of searching multiple environmental databases and no output schema, the description is incomplete; it does not explain the result format, pagination, or how many records are returned.

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 only repeats the parameter name and type ('query: keyword(s) to search'), adding no extra meaning about format, length, or examples.

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 multiple environmental data sources (USGS, NOAA, FEMA, GBIF, EPA) and returns record_ids for purchase, distinguishing it from sibling search tools that focus on other domains.

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 lists data sources but provides no guidance on when to use this tool versus alternatives, such as when a specific source is needed or 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?

No annotations are provided, so the description must disclose all behavioral traits. It describes the return fields but does not confirm read-only status, authentication needs, or potential side effects. The lack of explicit safety cues is a gap.

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 plus an Args section, no wasted words. Information is front-loaded with purpose, then returns, then usage hint. Very 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?

For a search tool with one parameter and no output schema, the description covers what it searches, what it returns, and directs to get_record for details. No missing crucial information.

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

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

With 0% schema description coverage, the description compensates by providing an example ('navy radar') and explaining that query expects keywords. This adds more value than the bare 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 the tool searches US federal spending across three specific sources (USAspending, SAM.gov, Grants.gov) and lists returned fields. This distinguishes it from sibling tools like search_cyber_threats and search_research_papers.

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 mentions using get_record for full records, providing a clear context. However, it does not specify when to use this tool over siblings or any exclusions, leaving some ambiguity.

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

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

With no annotations provided, the disclosure of 'Returns record_ids to buy' adds behavioral context beyond a simple search. However, it does not mention potential rate limits or authentication requirements.

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 purpose, followed by examples in a clear structure. Every sentence is substantive, with no redundant or irrelevant 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?

The description covers the search scope and return value (record_ids to buy), but does not detail result pagination or format. Given the lack of output schema, slightly more detail on the result could 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?

The 'Args' section provides concrete examples of valid queries (provider name, NPI, drug name, etc.), which adds meaning beyond the bare schema definition of 'Query'.

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 specifically states 'Search US healthcare data' and enumerates the specific databases (NPI, RxNorm, ClinicalTrials.gov, FDA recalls). This clearly distinguishes it from sibling tools like search_consumer_protection which target different domains.

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 indicates when to use this tool (for US healthcare data searches) but does not explicitly mention when not to use it or list specific alternatives for non-healthcare 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?

With no annotations, the description must fully disclose behavior. It describes the corpus and return fields, but lacks details on pagination, rate limits, query syntax limitations, or whether results are ranked.

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 short paragraphs: first covers purpose and output, second describes parameter. No unnecessary words, front-loaded with key 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?

Given the tool's simplicity (1 param, no output schema), the description covers purpose, input, and output fields adequately. Missing details like error handling or result limits, but these are minor.

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

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

Schema has 0% coverage for the single parameter. The description adds examples ('miranda arizona' or 'ninth circuit qualified immunity') which clarifies usage, but does not specify allowed syntax or operators.

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 court opinions and SEC releases, specifies the scope (1.5M+ opinions, specific courts), and lists return fields. It also distinguishes from sibling get_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 mentions 'Use get_record for the full opinion record' as an alternative, and provides example queries. However, it does not specify when to avoid using this tool.

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. Description mentions return fields but does not disclose any behavioral traits such as rate limits, authentication requirements, or potential restrictions. It assumes read-only access but doesn't confirm.

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 well-structured with purpose first, followed by use cases and parameter details. However, the sentence about returns could be integrated more concisely.

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 simplicity of a single-parameter search tool with no nested objects or output schema, the description covers purpose, usage, return fields, and alternative tool comprehensively.

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?

Only one parameter 'query' with 0% schema description coverage. Description adds concrete examples of valid queries (e.g., 'lithium battery cathode', patent number) and explains the parameter's role, compensating for the missing schema 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?

Description clearly states the tool searches granted US patents by multiple criteria (title, assignee, inventor, number, technology area). It distinguishes from sibling tools like search_ai_models and search_legal_cases which target different domains.

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 monitoring, portfolio lookup. Also directs user to get_record for full records, serving as a clear alternative.

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. Discloses that output is record_ids 'to buy', implying a purchase requirement, but does not explain pricing, authentication, rate limits, or side effects. Minimal 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?

Short description (two sentences plus 'Args' line). Purpose is front-loaded. However, the 'Args' section is redundant with the schema, and the description could be more tightly written.

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 or annotations, description should compensate. It does not explain what 'buy' means, how to use record_ids, or what happens after purchase. Incomplete for a tool with transactional implications.

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?

Only one parameter 'query' with schema type string. Description adds 'keyword(s) to search', which barely supplements the schema. No additional detail on query format, operators, or constraints.

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 it searches US regulations and law, listing specific sources (federal court opinions, CFR, Federal Register, etc.) and mentions output (record_ids to buy). Distinguishes from sibling search tools by scope, but could be more explicit about what makes it 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?

No guidance on when to use this tool versus other search tools (e.g., search_consumer_protection, search_sec_filings). Does not specify prerequisites, context, or limitations.

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 must disclose behavioral traits. It mentions sources, return fields, and record_id usage but omits details like rate limits, authentication, pagination, or read-only nature. Adequate but not comprehensive.

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: one paragraph for purpose and return fields, another for the argument. No unnecessary words, well-structured, and important 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 the tool's simplicity (one parameter, no output schema), the description covers essentials but lacks details on result limits, pagination, or ordering. Adequate for basic use but could be more 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 description adds meaning to the single parameter 'query' with an example, compensating for the schema's 0% coverage. It explains the purpose and provides a concrete usage example, though could detail query syntax.

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 scholarly works across multiple databases with a specific scope (1.2 million+). It distinguishes from sibling tools like get_record and other search tools by specifying the resource domain.

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 implicitly tells when to use this tool (for searching scholarly works) and explicitly directs to use get_record for full records. It does not explicitly state when not to use but sibling names provide differentiation.

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 for behavioral disclosure. It does not mention read-only behavior, authentication needs, rate limits, or pagination, which are important for safe invocation. The description only lists return fields but lacks operational details.

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 (4 sentences) and well-structured: first sentence states purpose and types, second describes output, third references a sibling tool, last documents the parameter. Every sentence adds value with 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?

For a tool with one parameter and no annotations or output schema, the description is mostly complete—it covers search scope, return fields, and relationship to get_record. However, it lacks information on result limits, error behavior, or whether the search is real-time, which would fully contextualize 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 alone parameter 'query' has no schema description (0% coverage), but the tool's args section adds meaning by describing it as 'keyword(s) to search' and providing an example. This compensates for the bare schema, though it could be more detailed (e.g., format, max length).

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' and enumerates specific filing types (13F, Form 4, 8-K/10-K, XBRL, EFTS, litigation, proceedings), making the tool's purpose unambiguous. It also distinguishes the tool from sibling 'get_record' by directing users to use that for full filing retrieval.

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 an example query ('Berkshire 13F') and notes that for full filings one should use 'get_record'. However, it does not explicitly state when not to use this tool or provide alternative scenarios, leaving some interpretation to the agent.

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