Scrutica

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 outputs include bounds and an is_estimated flag, and warns against treating them as exact. This adds transparency about the probabilistic nature of the estimate. No destructive behavior is indicated, but auth or rate limit info is absent.

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: purpose, return content, usage caution. Every sentence provides value with no redundancy. Ideal front-loading.

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 compute tool with 5 parameters and no output schema, the description covers the return value shape, methodology link, and usage caveat. It does not explain all parameters, but the schema fills that gap. Slight improvement could mention potential applications.

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 60% (3 of 5 parameters have descriptions). The tool description does not add significant information beyond the schema; it reiterates the overall purpose without deepening parameter understanding. Baseline 3 is appropriate.

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

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

The description clearly states it computes peak BF16 FLOP estimates for a hardware configuration, specifying the return structure and methodology reference. This distinguishes it well from sibling tools which fetch static data (e.g., company, facility).

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 against presenting outputs as exact measurements and to relay bounds and the is_estimated flag. It references the methodology explorer for deeper context. However, it does not explicitly state when to use this tool versus alternatives, though siblings are clearly different in purpose.

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 return fields (legal name, country, org type, references, supply-chain counts). No annotations provided, so description covers safety profile adequately. However, does not mention error handling 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?

Three sentences: first states purpose, second lists returns, third gives sibling alternatives. No unnecessary words, front-loaded with key 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?

Simple tool with one required parameter, fully documented in schema. Sibling differentiation in description. Return fields listed. No output schema needed given the clarity of returns.

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?

Input schema has 100% coverage of the single parameter (company_id) with full format description. The tool description adds no additional explanation beyond what the schema already provides, so it meets the baseline but does not exceed.

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 explicitly states verb 'fetch', resource 'complete details for a single organization', and specifies allowed entity types (company, government entity, holding company). Also distinguishes from siblings by naming alternative tools for different purposes.

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 clear when-to-use guidance: directs to scrutica_query_export_controls for BIS designations and scrutica_get_supply_chain for edge graphs. Also advises to resolve IDs via scrutica_search before 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?

Despite no annotations, the description comprehensively lists all return fields (operator, owner, country, power capacity, GPU inventory, etc.), including derived data like BIS Entity List exposure. This gives full behavioral transparency.

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

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

Two sentences: first states purpose, second lists return fields. No wasted words, front-loaded with actionable 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?

Without an output schema, the description provides a thorough list of all returned fields, covering all relevant aspects of a facility. It is complete for a single-fetch 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 100% with one parameter fully described. The description adds meaningful guidance beyond the schema by repeating the format and reinforcing the 'resolve via search' rule.

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 'Fetch complete details' for a single Scrutica facility, specifying the resource and action. It distinguishes from sibling tools like scrutica_search (which resolves IDs) and other get-entity 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 instructs to resolve facility IDs first via scrutica_search and warns against guessing slugs. This provides clear prerequisites and when-not-to-use 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 full burden. It explains the tool returns a URL, anchor, and summary, which is basic behavioral info. No mention of destructive actions, auth, or side effects, which seems appropriate for a read-only documentation retrieval 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 three sentences, is front-loaded with the purpose, and every sentence adds value. 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?

Given a single parameter with enums, no output schema, and no annotations, the description covers purpose, parameter topic, usage hint, and return format. It is complete for the tool's simplicity.

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?

There is one parameter (topic) with an enum. Schema description coverage is 0%, but the description lists and contextualizes the enum values by stating they are topics for methodology. It adds meaning beyond the raw enum by providing context.

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 methodology documentation for a Scrutica metric or index, lists specific topics, and describes the return format (URL + anchor + summary). This is specific and distinguishes it from sibling 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 says 'Use this when a user asks how did you calculate X', providing clear when-to-use guidance. It does not explicitly state when not to use, but siblings are sufficiently different to imply the 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 describes the return contents (summary, assumptions, facilities, timeline, citations), implying a read-only operation. Lacks explicit safety or side-effect notes, but adequate for a fetch 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 sentences, first states purpose, second lists scenarios and return structure. No extraneous information; efficient and 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?

Complete for a simple lookup tool with one parameter and no output schema. Covers available scenarios and return elements. Could mention response format or limitations, but not necessary.

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?

Adds meaning to the enum values beyond the schema by explaining what each scenario represents (e.g., 'taiwan-strait': 4 TSMC disruption scenarios). Semantics are enriched for the single 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?

Clearly states it fetches geopolitical compute risk scenarios and lists available scenarios. Distinguishes from sibling tools which focus on other entities (companies, facilities, sovereign programs, supply chains, export controls, search).

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 context on when to use: when needing a predefined scenario summary. Does not explicitly state when not to use or name alternatives, but the sibling list implicitly clarifies 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?

With no annotations provided, the description discloses the fetch operation and return fields but lacks details on safety (e.g., idempotency), authentication needs, rate limits, or error handling. The description is adequate but not thorough.

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, consisting of two sentences that clearly convey purpose and key behavior. No redundant or extraneous 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 no output schema, the description compensates by listing return fields. However, it does not address edge cases like non-existent programs or error states. For a data retrieval tool with multiple parameters, it is reasonably complete but leaves minor gaps.

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

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

Schema coverage is 100%, so parameters are already documented in the input schema. The description adds value by listing the return fields for a single record, which is not in the schema, and explaining the list_all parameter's effect. This provides context beyond schema definitions.

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 specific verb ('Fetch') and resource ('national sovereign AI compute program') with detailed return fields. It differentiates from siblings by focusing on sovereign programs, distinct from other scrutica_get_* tools for companies or facilities.

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 explicit guidance on when to use this tool versus alternatives like scrutica_get_company or scrutica_get_facility. The description implies use for sovereign program data but does not provide when-not or alternative scenarios.

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 details what each direction returns, the components of an edge, the data sources (licensed database plus SEC Exhibit 21), snapshot date, and data volume (20,534 edges). This is comprehensive and beyond minimal 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 compact yet informative, with each sentence adding value. It begins with the core purpose, then explains directions, edge fields, and data source. No unnecessary content, but the structure could be slightly improved by separating the edge field details into a list.

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 3 parameters, no output schema, and no annotations, the description provides sufficient context: direction options, edge structure, data provenance, and data size. It does not cover pagination or limit behavior (though limit is defined in schema), but overall it's complete for most agent use cases.

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 only 33% (only org_ids described). The description adds significant value by explaining the 'direction' parameter values, the format of org_ids, and the meaning of edge fields. However, it does not describe 'limit' or 'output' schema, but the parameter semantics are well enhanced.

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 it returns supply-chain relationships for organizations. The direction parameter is explained in detail. The scope is well-defined and distinct from sibling tools like scrutica_get_company or scrutica_get_facility.

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 when to use the tool (when supply-chain relationships are needed) and explains the three direction options. However, it does not explicitly state when not to use it or mention alternatives, but sibling tools are clearly different.

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 responsibility for behavioral transparency. It explains that the tool uses fuzzy matching on entity_name, returns specific fields, and notes the data source authority tier. This adds valuable context beyond the schema, though it lacks details on rate limits or error handling.

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 adding essential information: purpose, matching and return fields, and authority tier. It is front-loaded and concise without any superfluous content.

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 lack of annotations and output schema, the description covers key aspects: queried lists, matching method, return fields, and data source tiering. It could mention the limit parameter and optional country, but overall it provides sufficient context for a lookup 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 input schema already provides descriptions for 3 of 4 parameters (75% coverage). The description only reinforces the fuzzy matching for entity_name, not adding new semantic meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: looking up export control designations from BIS Entity List, OFAC SDN, and Wassenaar Arrangement CCL. It specifies the verb 'look up' and the resource 'export control designations for companies or countries', distinguishing it from sibling tools like `scrutica_search` or `scrutica_get_company`.

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 export control lookups and explains the fuzzy matching behavior, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. The context is clear enough for most use cases.

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 burden but only describes basic search behavior. It does not cover potential behavioral aspects like rate limits, auth requirements, or performance implications. However, for a read-only search tool, the description 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 concise, with three sentences that front-load the main purpose and use bullet points for clear directives. Every sentence adds value without 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 4 parameters and no output schema, the description covers the return fields and provides usage context. It lacks explicit mention of the limit parameter, but overall it is sufficiently 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?

The description adds meaning to the entity_type and country parameters beyond the schema by stating their purpose (scoping to a single class, filtering by ISO code). With 75% schema coverage, the description compensates for undocumented parameters.

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

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

The description explicitly states the tool searches facilities, companies, or sovereign programs by free-text query, and returns specific fields. It also distinguishes itself from sibling tools like scrutica_query_export_controls and scrutica_get_supply_chain, making the purpose very clear.

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

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

The description provides explicit guidance on when to use and when not to use the tool, including alternatives for export-control lookups and supply-chain traversal. It also explains filtering options by entity_type and country code.

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