semi-data

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

With no annotations, the description must carry the full burden of behavioral disclosure. It does reveal the data source, scope (federal, quantum), and output nature (shows organizations, amounts, agencies). However, it omits any mention of safety characteristics like read-only status, pagination, rate limits, or edge cases, leaving some behavioral ambiguity.

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

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

The description is two sentences, front-loaded with 'US federal contracts and grants for quantum technology,' and every clause earns its place. It efficiently conveys domain, data source, output content, and a use case with zero fluff.

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 nine optional parameters and no output schema, the description handles context well by describing what the output contains (organizations, amounts, agencies) and the political/economic context (2026 executive orders). Missing are explicit mentions of pagination or result format, but the essential operational picture is sufficiently complete.

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

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

Schema description coverage is 100%, so the schema already fully documents all nine parameters. The description adds no parameter-specific semantics beyond what the schema provides; it repeats the notion of searching by recipient, agency, and amount but does not elaborate on syntax or formats.

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 returns US federal contracts and grants for quantum technology, with a specific data source (USAspending.gov) and covered agencies (DOE, NSF, DARPA/DoD). This distinguishes it from all sibling tools, which focus on trade flows, minerals, regulatory signals, restricted entities, and 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 provides an explicit use case ('Use to track the quantum supply chain and policy push downstream of the 2026 quantum executive orders'), giving clear context for when to apply the tool. However, it does not explicitly state when not to use it or name alternative tools, though siblings are clearly domain-distinct.

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 partially carries the transparency burden. It discloses the data source (UN Comtrade), time granularity (annual), metric (export volumes), and dimensions (country, HS code, material). However, it does not mention output format, pagination defaults, ordering, or any limitations, which is a notable gap for a data-query 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 a single, tightly written sentence that front-loads the key information (UN Comtrade annual export volumes) and states the filtering dimensions concisely. Every word contributes to the meaning, with zero filler.

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

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

Given the tool has 7 parameters, no output schema, and no annotations, the one-line description is insufficiently complete. It does not describe return values, result structure, or common usage patterns. Users are left to guess what the API actually returns and how to handle the parameters 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?

Schema description coverage is 100%, so the baseline is 3. The tool description adds limited semantic weight beyond the schema—it groups the parameters under 'semiconductor-critical materials' but does not explain how parameters interact (e.g., material vs hs_code). Thus, no significant additional meaning is provided.

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

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

The description clearly identifies the tool as retrieving UN Comtrade annual export volumes, filtered by country and HS code, specifically for semiconductor-critical materials. This is a specific verb+resource+scope that distinguishes it from sibling tools focused on funding, mineral deposits, regulations, restricted entities, and 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 provides no explicit guidance on when to use this tool versus alternatives. It states the tool's function but does not offer exclusions, prerequisites, or comparisons to sibling tools. Users must infer usage solely from the purpose statement.

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 adds a critical behavioral note: the data is a 2011 historical snapshot and instructs the user to use as_of in the response meta. This is beyond what the schema conveys. It does not cover all behaviors (e.g., pagination or response structure), but the caveat is substantive.

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

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

The description is two sentences: the first front-loads the resource and scope, the second provides a necessary historical caveat. It is efficient with no fluff, earning a high score for conciseness and structure.

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 9 parameters, no output schema, and no annotations, the description provides sufficient context for basic use: data source, focus, and a key caveat about data age. It lacks explicit return value details, but the schema covers parameters and the description hints at response meta.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific meaning beyond mentioning 'semiconductor-critical materials,' which hints at commodity/min_score but is not explicit. The schema already describes each parameter adequately.

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 USGS MRDS mineral deposit records for semiconductor-critical materials, with a specific count of 1,233 deposits. It is specific and easily distinguishable from the unrelated sibling tools (e.g., quantum funding, trade flows).

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 domain is clearly mineral deposits, and the sibling tools are unrelated, making the context obvious. However, there are no explicit when-to-use vs. alternatives or exclusions, though the description's focus on semiconductor-critical materials implies the intended use case.

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 results are LLM-structured with importance scoring, which is valuable behavioral context. However, it does not describe the return format, pagination, or any other operational behavior, leaving gaps for a tool with no output schema.

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

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

The description is two sentences with no wasted words. It front-loades the core purpose and adds the key behavioral nuance (LLM-structured, importance scoring) succinctly.

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 and no annotations, the description should explain return values and operational details. It covers the domain and a key output characteristic, but does not explicitly state what the tool returns or how results are paginated. The schema covers inputs well, but the description does not fully compensate for missing output 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?

The input schema provides full descriptions for all 9 parameters, so the baseline is 3. The description adds a high-level mention of signal types and materials, but this does not meaningfully enhance parameter understanding 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 identifies the tool's purpose: searching Federal Register regulatory signals on semiconductor-critical materials, with explicit signal types (export controls, sanctions, etc.). It distinguishes itself from sibling tools by focusing specifically on regulatory signals for materials, unlike funding, trade flows, mineral deposits, restricted entities, or 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 implies is usage when a user needs regulatory actions on semiconductor materials, but it does not explicitly state when to use it over alternatives or provide exclusions. No alternative tools are mentioned, so the guidance remains implicit rather than explicit.

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 of behavioral disclosure. It only names the data source and use case, without mentioning any behavioral traits such as full-text search semantics, result set behavior, pagination, or potential limitations. The description adds little beyond what the schema already implies.

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 extremely concise, consisting of two short sentences. It front-loads the key identifier ('US Consolidated Screening List') and immediately states the intended use, with no wasteful or redundant text.

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 tool has 6 parameters and no output schema or annotations. The description provides a clear use case and data source, which is enough to differentiate it from siblings. However, it lacks any detail about what the search returns, how matches are scored, or whether special list filtering is required, leaving some contextual gaps for a tool used in due diligence.

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 100% parameter description coverage, so the baseline is 3. The description does not add any parameter-level meaning; it only provides a top-level context that the list is the US Consolidated Screening List. This context slightly enhances the meaning of 'list_name' but is not essential.

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

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

The description clearly identifies the tool as operating on the US Consolidated Screening List, which is a restricted-entity search. The intended purpose (supplier/counterparty due diligence) is explicit. However, it doesn't use a direct verb like 'searches' or 'queries,' leaving the action slightly implied by the tool name.

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 a clear use case: supplier/counterparty due diligence against export-control and sanctions lists. This differentiates it from sibling tools (e.g., trade flows, mineral deposits). It doesn't explicitly state when not to use it, but the context is sufficiently specific.

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 of behavioral disclosure. It adds a useful behavioral trait: filings are pre-processed with an LLM to produce supply chain signals, sentiment, and material mentions, rather than returning raw filings. However, it does not mention rate limits, permissions, pagination behavior, or whether results are read-only, leaving significant gaps 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?

The description is a single compact sentence with no filler or repetition. It front-loads the core resource (SEC EDGAR filings) and the value-add (LLM processing for supply chain signals). While it is short relative to the tool's complexity, it is not bloated and earns its place.

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

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

This is a complex tool with 10 parameters, no output schema, and no annotations. The one-sentence description is insufficient: it does not explain how the fields combine (e.g., are filters ANDed?), what the returned data structure looks like, or how to interpret the LLM-extracted signals. The rich parameter schema compensates for filter meanings but not for overall tool behavior and result expectations.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides, though it introduces the key concepts (supply chain signals, sentiment, material mentions) that map to the `sentiment` and `material` parameters. This adds minimal semantic value but does not compensate for any missing schema info.

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 tool name 'search_sec_filings' and description identify the resource (SEC EDGAR filings) and the function (extract supply chain signals, sentiment, and material mentions via LLM processing). It is clearly distinct from sibling tools that cover funding, trade flows, mineral deposits, and regulatory signals. However, the description itself does not explicitly state the verb 'search' or 'query', relying on the tool name to imply the action.

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 no guidance on when to use this tool versus alternatives such as search_regulatory_signals or search_mineral_deposits. There is no mention of exclusions, prerequisites, or complementary tools. Usage context is only implied by the resource type (SEC filings), which is weak guidance for an agent choosing among sibling search tools.

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