Slacking.biz — SEC Financial Data + US Economics + Demographics + FX

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

No annotations are provided, so the description must carry the burden. It mentions efficiency but fails to disclose limits, result format, read-only nature, or any constraints beyond the 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 concise but overly terse—a single sentence with no structure (title, sections). While short, it omissions reduce its usefulness.

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 simple input (one parameter, no output schema), the description should clarify what the batch query returns. It does not, leaving a crucial gap in 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 schema covers the single parameter 'tickers' with a description and example. The tool description adds no additional meaning beyond the schema, so 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 'Batch query multiple tickers at once for efficiency' indicates batching and efficiency, but 'query' is vague—it doesn't specify what data is retrieved. It distinguishes from single-ticker siblings but lacks precision.

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 alternatives like individual queries or get_all_financial_data. The description implies batching for efficiency but provides no explicit context or trade-offs.

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, and the description does not disclose behavioral traits like data freshness, latency, output format, or whether it returns standardized metrics. The description is too vague for the agent to understand important execution 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 a single clear sentence with no extraneous information, perfectly concise 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?

No output schema exists, and the description fails to indicate what return values look like (e.g., which metrics, format). The tool's name 'compare_companies' suggests a comparative analysis, but the description omits crucial contextual details about the comparison output.

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 adds no extra meaning beyond what the schema already provides for the 'tickers' parameter.

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

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

The description clearly states the verb 'compare' and the resource 'financial metrics across multiple companies side by side', effectively distinguishing it from sibling tools like batch_query or get_all_financial_data.

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

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

No explicit guidance on when to use this tool versus alternatives such as batch_query, industry_comparison, or screen_companies. The description implies usage for side-by-side comparison but lacks context or exclusions.

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 transparency burden. It discloses that no key is required and identifies the data source, but it does not mention rate limits, return format, or whether the data is current or historical. This is a minor gap for a simple read-only 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, front-loaded sentence with no wasted words. It conveys the purpose, scope, source, and authentication status efficiently.

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 only two optional parameters and no output schema, the description is largely complete. It specifies what is returned (median household income by geography) and source. However, it does not explain the response shape or whether it returns a single value or a list, but this is inferable from the 'by geography' phrasing and the limit parameter.

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

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

The input schema provides 100% coverage of both parameters (level and limit), including defaults and allowed values. The description's mention of geographies mirrors the level parameter but adds no additional meaning beyond the schema.

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

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

The description clearly and specifically states the tool's function: retrieving US median household income by geography. It names the metric, the geographic levels, and the data source, distinguishing it from siblings like datausa_population.

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 clear context: what data it returns, the geographies it covers, and that no API key is required. It does not explicitly mention alternatives or when-not-to-use, but the specificity implies usage for income-related queries.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions the data source and that no API key is needed, but omits response format, default behavior, or potential limitations. This is too minimal for full transparency.

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

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

The description is a single, front-loaded sentence that states the core purpose and key constraint (no key). Every word adds value, with no filler or redundant 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 tool's simplicity (two optional parameters, no output schema), the description covers the basic purpose and data source. However, without an output schema, it does not clarify whether the response is a list, a single number, or how results are grouped, leaving some ambiguity.

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

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

The input schema fully describes both parameters (level and limit) with 100% coverage. The description repeats the geography levels but adds no additional meaning beyond the schema, so the baseline of 3 applies.

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 retrieves US population data by geography, explicitly listing Nation/State/County/Place and citing the Data USA API with curated Census ACS data. This distinguishes it from sibling tools like datausa_income and other demographic retrievers.

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 is provided on when to use this tool versus alternatives. It does not mention that datausa_income covers income data or that other geography-specific tools exist. The 'No key required' note hints at ease of use but does not establish selection criteria.

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 adds useful context by noting the source is public-domain and requires no key. However, it does not disclose potential rate limits, response format, or limitations (e.g., date filtering, pagination), so basic transparency is present but not rich.

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 value: the first states the core function, the second differentiates from per-company search, and the third gives source/auth context. It is front-loaded with the most important information and contains no filler.

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

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

For a simple search tool with no output schema, the description covers the purpose, scope, differentiation, and authentication. It does not mention return value details or behavioral limitations, but given the simplicity and full parameter schema coverage, it is reasonably complete.

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

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

Schema description coverage is 100%, with both parameters (q and limit) already described in the schema. The tool description adds little beyond the schema, repeating the keyword/phrase concept but not providing extra context about query syntax or result behavior. 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 performs 'full-text search across ALL SEC EDGAR filings' and explicitly differentiates from the existing per-company SEC integration. The verb 'search' with a specific resource and scope makes the purpose unmistakable and distinguishes it from sibling tools like search_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 says it 'Extends the existing per-company SEC integration with cross-company filing search,' which gives clear context on when to use this tool instead of the per-company alternative. However, it does not explicitly name sibling tools or state when not to use it, missing the full when/when-not guidance.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source and licensing (Eurostat API, free with attribution), which is useful. However, it does not describe the response format (e.g., single value vs. time series), rate limits, or behavior for invalid geo codes. More behavioral detail would improve transparency.

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

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

The description is two concise sentences: the first states the core functionality with specific details, the second adds source and licensing info. Every word earns its place, and the most 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 optional parameter) and full schema coverage, the description is mostly complete. However, there is no output schema, and the description does not clarify whether the tool returns a single year's value or a historical series. This ambiguity prevents a higher score.

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

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

The schema description fully covers the single 'geo' parameter, including examples and default. The tool description repeats these examples without adding additional meaning. With 100% schema coverage, the baseline is 3, and the description does not exceed that.

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 Eurostat GDP data, specifying the metric ('annual, million EUR, current prices') and geographic scope ('EU country or aggregate'). It distinguishes itself from sibling tools like eurostat_inflation and eurostat_unemployment by naming the exact data type, and from generic GDP tools by sourcing from Eurostat.

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 clear context on when to use this tool: for annual GDP figures in million EUR at current prices for EU countries/aggregates. It does not explicitly mention alternatives or exclusions, but the specificity of the scope makes the usage context reasonably clear.

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

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

With no annotations, the description must carry the burden. It discloses the data source and measurement unit, but omits details about return format (e.g., time series vs. latest value), data freshness, or error behavior for invalid geo codes. This is a minimal but not fully transparent explanation.

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?

A single, front-loaded sentence that conveys the core purpose, the metric, example inputs, and the data source. Every phrase earns its place; there is no redundancy or 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?

The tool is simple (one optional parameter, no output schema). The description states the exact metric and examples, and the source adds credibility. It lacks a mention of the response structure (e.g., whether it returns a series or a single value), but for the simplicity level, the description is largely sufficient.

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

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

Schema description coverage is 100%, and the schema's geo parameter description already includes examples and a default. The description repeats the examples without adding new meaning such as accepted formats or additional constraints, so it provides no significant value 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 starts with a specific verb 'Get', identifies the resource 'Eurostat HICP inflation', and specifies the metric 'annual rate of change (%)' plus example geo codes (DE, FR, IT, EU27_2020, EA20). This clearly differentiates it from sibling tools like eurostat_gdp or get_inflation.

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 context by naming the specific source (Eurostat dissemination API) and metric (HICP inflation). It does not explicitly state when not to use this tool or name alternatives, but the specificity is enough to guide selection among siblings.

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

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

No annotations are provided, so the description carries the full burden. It discloses source, frequency, adjustment, and age scope, but does not describe return structure, potential errors, or whether it returns a single value or a time series. It implies read-only via 'Get' but does not state it.

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 sentence that contains all essential information: metric, frequency, adjustment, age, example codes, and source. There is no waste or redundancy.

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

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

The tool is simple with one optional parameter and no output schema. The description covers key dataset attributes, but it does not explain the return format or pagination for time-series data, which could be relevant for an API response.

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

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

The single parameter 'geo' is already fully documented in the schema with examples and a default. The description repeats those examples without adding new meaning beyond what the schema already provides.

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 starts with 'Get Eurostat unemployment rate (%) using a specific verb and resource, clearly distinguishing it from sibling tools like eurostat_gdp and eurostat_inflation. It specifies the exact metric, frequency, adjustment, and age range.

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 the exact dataset characteristics (monthly, seasonally adjusted, age 15-74) and lists valid geo codes, implying when it should be used. However, it does not explicitly mention alternatives or exclusion conditions.

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 of disclosing behavioral traits. It adds context by noting the source ('openFDA (public-domain US FDA data)') and includes a disclaimer ('not validated for clinical/production use'), which is valuable. However, it does not explicitly state that the operation is read-only, nor does it describe response formatting or pagination, leaving some behavioral aspects unclear.

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 long: the first states the core function, and the second adds source and disclaimer. Every sentence earns its place, with no filler. It is front-loaded with the most important information and appropriately sized for a tool with two simple parameters.

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 two parameters and no output schema, the description is mostly complete. It covers the search scope, data source, and a critical caveat. It does not explicitly describe the return format, but given the tool's simplicity and the presence of the disclaimer, the missing details are minor and unlikely to cause misuse.

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's mention of 'brand/generic name or ingredient' largely repeats what the query parameter schema already provides ('e.g. aspirin, brand name, ingredient'). It does not add meaningful new information about parameter usage beyond the schema.

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

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

The description clearly states the tool's function: 'Search FDA drug labels by brand/generic name or ingredient.' It uses a specific verb and resource, and the scope (by name/ingredient) distinguishes it from sibling tools like fda_recalls, which search a different FDA dataset.

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

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

The description provides no guidance on when to use this tool versus alternatives such as fda_recalls or other search tools. There is no mention of exclusions, prerequisites, or scenarios where another tool would be more appropriate. The intended usage is implied by the name but not explicitly contrasted with siblings.

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

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

With no annotations provided, the description must carry the transparency burden. It states the source (openFDA) and lists output fields, but does not disclose behavioral traits such as result ordering, data freshness, pagination, or rate limits. The 'Get' verb implies a read-only operation, but that is not explicit.

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 convey the tool's purpose, output fields, and data source with no filler. The key information is front-loaded, making it easy to scan.

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 is simple (one optional parameter, no output schema), and the description covers its purpose, output fields, and source. It lacks explicit mention of 'recent' definition or limit behavior, but these are not essential given the schema and overall simplicity. Slightly more detail on expected output format would be useful but is not critical.

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

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

The schema has 100% coverage for the single optional parameter 'limit', so the description is not required to explain it. However, the description adds no additional parameter context beyond what the schema already defines, earning the baseline 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 uses a specific verb ('Get') and resource ('FDA drug enforcement recalls'), and lists the key fields returned (product, reason, classification, status, company). This clearly distinguishes it from sibling tool fda_drug_labels, which deals with drug labels.

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 retrieving recall data but does not explicitly state when to use this tool versus the related fda_drug_labels or other data tools. There are no exclusions or alternative tool mentions.

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 is the sole source for behavioral traits. It mentions sources (ECB + 84 central banks) and free commercial use, but omits key details such as rate update frequency, rounding behavior, or any limitations (e.g., maximum amounts). This leaves significant gaps in understanding the tool's operation.

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: two sentences with front-loaded purpose ('Convert an amount...') followed by essential source details. Every sentence adds value, and there is no redundancy or 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?

Given three optional parameters, no output schema, and no annotations, the description provides minimal but adequate context for a simple conversion tool. However, it lacks details on return format, error handling, or rate source freshness, making it insufficient for complex 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 coverage is 100% with each parameter having a description. The tool description adds no new meaning beyond the schema defaults (e.g., amount default 1.0). Since schema covers all parameters adequately, the baseline score of 3 is appropriate.

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

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

The description explicitly states the tool converts an amount between currencies at current exchange rates, clearly identifying the verb 'convert' and the resource (amount, currencies). It distinguishes itself from sibling tools like fx_get_rates or fx_list_currencies by focusing on conversion rather than rate listing or currency listing.

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 currency conversion at current rates, but it does not explicitly mention when to use this tool versus alternatives like fx_get_pair_rate or batch_query. No when-not-to-use guidance is provided, relying on implied 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?

With no annotations, the description carries full burden. It mentions sources and free commercial use but omits behavioral traits like update frequency, precision, any limitations (e.g., not real-time), or return format. This gap is significant for safe invocation.

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, no fluff, clear and to the point. Each sentence adds value: action, data sources, licensing.

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 2-parameter tool with no output schema, the description is adequate but missing return format details (e.g., single number or object? date/precision?). It covers purpose and licensing but not the complete return behavior.

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

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

Schema coverage is 100% with descriptions for both parameters. The description reinforces the purpose but adds no new meaning beyond the schema. Baseline of 3 is appropriate.

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

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

The description uses a specific verb 'Get' and resource 'exchange rate between any two currencies', clearly distinguishing it from sibling tools like fx_convert (conversion amount), fx_get_rates (multiple rates), and fx_list_currencies (listing codes).

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 obtaining a single exchange rate pair but provides no explicit guidance on when to use this tool versus alternatives like fx_convert or fx_get_rates. It adds context about data sources and licensing but lacks usage direction.

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 data sources (European Central Bank + 84 central banks), number of currencies (201), and key features (no API key, commercial free). This is good but could mention rate update frequency.

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

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

Two sentences with no wasted words. Front-loaded with core purpose, then key details. Perfectly 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?

For a simple tool with only 2 optional parameters and no output schema, the description provides sufficient context. It covers purpose, sources, usage conditions, and parameter defaults adequately.

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 baseline is 3. The description adds context that 'quotes' empty means all currencies and base defaults to EUR, but these are already in schema descriptions. No additional meaning beyond schema.

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

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

The description clearly states it 'gets live exchange rates for any currency', specifies sources and coverage (84 central banks, 201 currencies), and distinguishes from sibling tools like fx_get_pair_rate and fx_list_currencies by implying broad vs specific functionality.

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 no API key needed and free for commercial use, which helps decide when to use. However, it does not directly compare to siblings or say when not to use, though the context makes it clear this is for broad live rates.

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 accurately states that the tool returns a list of 31 currency codes and names. This is sufficient behavioral transparency for a read-only list operation, though it does not mention 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 exceptionally concise, using two sentences to convey purpose, scope, and examples. No wasted words; the key information is front-loaded in the first sentence.

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 (0 parameters, no output schema, clear purpose), the description is complete. It explains what the tool does, the number of items, and examples. No additional information is necessary for correct 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 tool has no parameters (0 params, schema coverage 100%). The description adds no parameter information because none is needed. The baseline for 0 params is 4, and the description meets that expectation without adding unnecessary detail.

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 action ('list'), the resource ('currency codes and names'), and the scope ('all 31 available' with examples). It effectively distinguishes this tool from siblings like fx_convert or fx_get_rates by specifying that it provides the list of available currencies for foreign exchange queries.

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 this tool: when you need the list of available currencies for foreign exchange queries. However, it does not explicitly state when not to use it or mention alternatives like fx_get_rates. The context is clear but lacks exclusionary 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?

With no annotations provided, the description carries the transparency burden. It discloses the source (Treasury Reporting Rates of Exchange), that it is public domain and requires no key, and the update frequency (monthly). This goes beyond a generic 'get rates' description and gives the agent useful behavioral context. It does not describe error handling or output format, but these are less critical for a simple query.

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

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

The description is two sentences, front-loaded with the core action and resource, followed by essential contextual details (source, access, update frequency, relationship to a sibling). Every sentence adds value, with no redundancy or 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?

The tool is simple, and the description covers source, access, and update frequency. However, there is a sibling tool named treasury_fx_rates that likely deals with the same data, and the description does not disambiguate from it. Additionally, with no output schema, the description could have mentioned what the response contains (e.g., a single rate or a list), but it remains adequate for basic usage.

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

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

The schema description covers the only parameter (currency) fully, including examples and default value, so schema description coverage is 100%. The tool description adds no further parameter meaning. Therefore, the baseline score of 3 is appropriate per the rubric.

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 ('Get'), the resource ('official U.S. Treasury exchange rates'), and the unit convention ('units of foreign currency per USD'). It also explicitly names a complementary sibling (fx_get_rates), distinguishing this tool from that alternative. The source attribution adds further specificity.

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 clear context for when to use this tool: when official U.S. Treasury rates are needed, contrasting with ECB-based fx_get_rates. However, it does not explicitly state when not to use it or mention other sibling tools like treasury_fx_rates, which could be a direct alternative. The complementary relationship is implied rather than fully spelled out.

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 behavioral traits. It mentions the data source (OpenAQ) and pollutants, but does not disclose details such as whether data is real-time or historical, what the return format is, any API key requirements, or error behavior. This is a significant gap for a tool with no annotation coverage.

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, focused sentence that front-loads the core purpose and key details. No fluff or redundancy.

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

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

Given no output schema and no annotations, the description should explain return values and behavioral details. It only states the data categories and scope, leaving the agent without information on output structure, required parameters (location is optional), or potential failure modes. This is incomplete for a simple 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 schema fully covers the only parameter 'location' with examples, achieving 100% schema description coverage. The description adds context about pollutants but does not add 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: 'Get air quality data (OpenAQ) — PM2.5, PM10, O3, NO2 for a city.' It identifies a specific verb, resource, and the data scope (pollutants and city). This distinguishes it from sibling tools, none of which relate to air quality.

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 contexts for air quality data retrieval. It clearly specifies the scope (city and pollutants), making it obvious when to use the tool. However, it does not explicitly exclude alternatives or mention when-not-to-use scenarios, so it falls short of a 5.

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

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

With no annotations, the description carries full burden. It discloses that the tool returns three financial reports, but it does not mention any behavioral traits such as rate limits, data freshness, or whether the operation is read-only. The description is adequate but lacks depth.

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 sentence of 12 words, front-loading the purpose without any extraneous information. Every word earns its place.

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

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

For a tool with one parameter and no output schema, the description is minimal but sufficient for a simple retrieval. However, it does not describe the return format or structure, which could be needed given the lack of output schema and annotations.

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

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

The single parameter 'ticker' is described in the schema as 'Stock ticker symbol' (100% coverage). The description adds no additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves all financial data (income statement, balance sheet, cash flow) in one call, specifying the verb 'get' and the resource 'all financial data'. This distinguishes it from siblings like get_income_statement which return only one type.

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 obtaining all three statements simultaneously, but it does not explicitly state when to use this tool vs individual tools like get_income_statement. 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?

No annotations are provided, so the description must disclose behavioral traits. It only mentions the output categories (assets, liabilities, equity) but omits details like data freshness, authentication requirements, rate limits, or whether the operation is read-only.

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, concise sentence that immediately conveys the tool's purpose. It is well front-loaded with no unnecessary 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 the tool has only one parameter, no annotations, and no output schema, the description is minimally complete. It states what the tool returns but lacks behavioral context (e.g., whether data is real-time or historical). It could be richer without being verbose.

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

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

The input schema fully describes the ticker parameter as 'Stock ticker symbol' (100% coverage). The description does not add additional meaning beyond the schema, such as expected format or examples, resulting in a baseline score.

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 'Get', the resource 'detailed balance sheet', and the scope 'for a company' with a parenthetical listing of key components (assets, liabilities, equity). This distinguishes it from sibling tools like get_income_statement and get_cash_flow.

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 does not provide any guidance on when to use this tool versus alternatives, such as get_income_statement or get_cash_flow. No context about prerequisites, use cases, or exclusions is given.

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

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It only states what data is retrieved (US GDP from BEA) but does not describe the return format, data frequency, units, or any other behavioral traits. This is minimal and leaves significant 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 a single, front-loaded sentence that conveys the essential purpose without unnecessary words. It is appropriately sized for a tool with no parameters and no complex behaviors.

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 an output schema, the description should explain what the user receives, but it does not. It also does not differentiate from the sibling tool get_gdp, which could lead to confusion. The tool is simple, but the description omits important context about the data's nature and return value.

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

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

The tool has zero parameters, and the description does not need to explain any. According to the rubric, a zero-parameter tool receives a baseline of 4, and the description adequately handles this by not adding irrelevant parameter details.

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

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

The description clearly states the verb 'Get', the resource 'US GDP', and specifies the source 'BEA (NIPA accounts)', which distinguishes it from sibling tools like get_gdp or eurostat_gdp. This is a specific and unambiguous purpose.

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 when US GDP data from BEA is needed, but it does not explicitly state when to use this tool over alternatives like get_gdp or mention any exclusions. The source specification provides some guidance, but the lack of comparison with siblings limits clarity.

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 mentions 'detailed' and lists sections, but does not disclose any potential limitations, update frequency, or format. Adequate but not rich.

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?

Single sentence, no wasted words. Front-loaded with key purpose and scope.

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

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

For a simple tool with one parameter, the description covers the main function. Lacks details on return format, date range, or currency, but acceptable given no output schema.

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

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

Schema coverage is 100%, with the ticker parameter already described. The description adds no extra semantic value beyond context. Baseline 3 applies.

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 provides a cash flow statement and specifies the three sections (operating, investing, financing). It distinguishes from sibling tools like get_balance_sheet and get_income_statement.

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

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

Implies use when needing cash flow data for a company, but lacks explicit guidance on when to choose this over alternatives. The context of siblings makes the purpose clear enough.

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 only indicates a read operation but lacks details on side effects, authentication, or what 'summary metrics' entails.

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?

Single sentence, front-loaded with purpose, no wasted words; could be slightly more detailed but remains 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?

Without output schema, description provides high-level output (A-F grade, summary metrics) but lacks precise format or field details, leaving some ambiguity.

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%, description adds no extra meaning beyond the schema's parameter description (ticker with 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?

Clearly states 'Get' verb, resource 'company's overall financial health score', specifies 'A-F grade' and 'summary metrics', distinguishing it from siblings like get_balance_sheet.

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?

Implied usage as a quick health overview but no explicit when-to-use, when-not-to-use, or alternative recommendations among siblings.

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

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

With no annotations, the burden falls entirely on the description. It only lists return fields but does not disclose behavioral traits like rate limits, idempotency, or any side effects. The read-only nature is implied but not stated, leaving gaps for an AI agent.

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, clear sentence with no extraneous information. It efficiently conveys the tool's purpose and key outputs, making it easy to parse.

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

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

For a simple tool with one parameter and no output schema, the description adequately lists the data fields returned. However, it could be slightly more complete by specifying any limitations (e.g., US stocks only) or mention that it returns a single record.

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

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

Schema coverage is 100% and the parameter 'ticker' is documented. The description adds value by specifying the exact fields returned (sector, industry, employees, description, market cap), which goes beyond the schema and helps the agent understand what the tool provides.

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 identifies the tool as retrieving a company profile with specific attributes (sector, industry, employees, description, market cap). It distinguishes itself from sibling tools like 'get_all_financial_data' or 'compare_companies' by focusing on a summary profile, but could be more explicit about being for a single 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?

No guidance is provided on when to use this tool versus siblings such as 'compare_companies' or 'get_all_financial_data'. The description does not indicate context, prerequisites, 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 provided, so description carries burden. Mentions data source (Census Bureau) but lacks details on rate limits, data freshness, return format, or whether data is cached. Adequate for a simple lookup but minimal.

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?

Single sentence, no fluff, front-loaded with verb and resource. Extremely concise and structured.

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

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

Given low complexity (1 required parameter, no output schema, no annotations), description sufficiently covers purpose and parameter. Lacks return format but adequate for a straightforward 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 100%, and the fips parameter description includes an example and format. Tool-level description adds 'by FIPS code', but overall parameter info is well-documented by schema; description does not add significant extra meaning.

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 'Get county-level demographics data by FIPS code' with explicit source 'US Census Bureau'. Verb 'Get' + resource 'county-level demographics data' + method 'by FIPS code' distinguishes from sibling tools like get_zip_demographics.

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 when-to-use or when-not-to-use instructions. Context implies use when county demographics by FIPS are needed, but no guidance on alternatives like batch_query or zip-level data.

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 provides basic context: source (FRED) and that it returns multiple indicators. It does not disclose rate limits, data frequency, or return format, but the tool is read-only and parameterless, so the lack of detail is acceptable though 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?

A single clear sentence that efficiently conveys purpose and source. No wasted words, though it could be slightly more structured.

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

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

For a parameterless tool with no output schema, the description adequately covers purpose and source. It does not describe the return format, but given low complexity, this is sufficient. It could mention data recency or scope.

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 zero parameters and 100% schema coverage. The description adds meaning by naming the specific indicators retrieved, which goes beyond the empty schema. Baseline is 4, and the description meets it well.

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 multiple key economic indicators (GDP, CPI, rates, employment, housing) in one call from FRED. It distinguishes itself from sibling tools like get_gdp or get_rates by offering a combined endpoint, but could be more specific about the full set of indicators included.

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

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

The phrase 'in one call' implies using this tool when multiple indicators are needed, avoiding repeated calls to sibling tools. However, it does not explicitly state when to choose alternate specific tools instead, such as when only one indicator is required.

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

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

No annotations are present, so the description carries the full burden. It only states the basic function without disclosing behaviors like data freshness, read-only nature, 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?

Single sentence, front-loaded with key information, no wasted words. Extremely 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?

For a simple tool with no parameters and no output schema, the description is minimally adequate but lacks return format details. Could be more informative about what the output contains.

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 are no parameters (schema is empty with 100% coverage). The description adds no parameter info, but baseline for 0 parameters is 4.

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

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

The description clearly states the verb 'Get', the specific resources 'latest unemployment rate and jobless claims', and the source 'FRED'. It is distinct from sibling tools that cover other economic indicators.

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 is provided on when to use this tool versus alternatives. With many sibling tools for other economic data, explicit context or exclusions would be helpful.

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 behavioral traits. It indicates a read-only operation but does not mention any limitations, rate limits, data freshness, or what exactly 'available' means (e.g., are all types always returned?). The description is too minimal for full transparency.

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

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

The description is a single concise sentence that front-loads the action and object. Every part is essential, with no wasted words.

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

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

For a simple tool with one parameter and no output schema, the description adequately explains what it does, including examples. It could mention that return values are administrative filing type labels, but it is largely 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% as the only parameter 'ticker' has a description. The tool description adds no additional meaning beyond the schema, so 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 verb 'Get', the resource 'filing types available for a company', and provides specific examples (10-K, 10-Q, 8-K, etc.). This distinguishes it from sibling tools that deal with specific filings or financial data.

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

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

The description does not provide any guidance on when to use this tool versus alternatives like search_filings or get_all_financial_data. It implicitly covers its purpose but lacks explicit usage context or exclusions.

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 carry the burden. It indicates a read-only retrieval ('Get latest'), but lacks details on update frequency, rate limits, or data format.

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

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

The description is a single, clear sentence with no extraneous information, making it highly 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 parameterless tool with no output schema, the description adequately conveys the tool's purpose and data source. However, it does not specify the return structure or any time range.

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?

No parameters exist, and the schema coverage is 100% trivially. The description adds no parameter info, but baseline is 4 for zero-parameter tools.

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 (Get), resource (US GDP data), and source (FRED), distinguishing it from sibling tools that retrieve other economic indicators.

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 when US GDP data is needed, but does not explicitly mention when not to use it 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?

The description is minimal and does not disclose behavioral traits such as data update frequency, range, or any limitations. However, it correctly implies a read-only operation, and there is no annotation to contradict. It provides basic context but could elaborate on what 'latest' means.

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 sentence that conveys all essential information without unnecessary words. It is front-loaded 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?

Given the lack of output schema and annotations, the description should provide more context about what the tool returns, such as data format, date range, or units. Without it, the agent lacks sufficient information to use the tool correctly.

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 zero parameters and 100% schema description coverage, the description does not need to explain parameters. The baseline for 0 parameters is 4, and the description meets that by not requiring additional param details.

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

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

The description clearly specifies the action ('Get'), the resource ('latest housing starts and existing home sales data'), and the source ('FRED'). This clearly differentiates it from sibling tools like get_gdp or get_inflation, which cover different economic indicators.

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 is provided on when to use this tool versus alternatives. Sibling tools like get_econ_summary might also include housing data, but the description offers no context for selection or exclusions.

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 carry the full burden of behavioral disclosure. It does not disclose return format, data period (e.g., quarterly vs annual), geographic limitations, or whether additional details like footnotes are included. The description is too sparse for a financial 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 sentence with 12 words, directly front-loading the essential information. It is highly concise with no wasted words.

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

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

Given the tool's complexity (a financial statement with no output schema and multiple siblings), the description lacks important details such as the format of the output, historical depth, and how to interpret the data. It is insufficient for an agent to fully understand the tool's capabilities.

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% description coverage with a single parameter 'ticker' described as 'Stock ticker symbol'. The description adds no additional meaning beyond the schema, so it meets the baseline with no extra value.

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

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

The description clearly states the verb 'Get' and the resource 'detailed income statement for a company', listing key components (revenue, expenses, earnings per share). This distinguishes it from sibling tools like get_balance_sheet or get_cash_flow, which cover different financial statements.

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 get_all_financial_data or get_balance_sheet. There is no mention of prerequisites, limitations, or context for optimal 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 exist, so the description must convey behavioral traits. It only states the tool 'gets' data, implying a read operation, but does not disclose whether it is read-only, destructive, or has rate limits. Minimal insight beyond the verb.

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, concise sentence with no superfluous words. It efficiently conveys the core purpose without 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?

While the description states the purpose, it lacks details about the output format, data frequency, or structure. Since there is no output schema, the description should provide more context about what the returned data contains.

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

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

The tool has zero parameters, and the schema coverage is 100% (empty schema). Per guidelines, a tool with 0 parameters receives a baseline score of 4, as there is no need for additional parameter explanation.

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 'latest CPI/inflation data from FRED', specifying both the data type (CPI/inflation) and source (FRED). This distinguishes it from sibling tools like get_gdp or get_employment.

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. It does not explain any prerequisites, limitations, or contrasting use cases with siblings such as get_econ_summary or get_rates.

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

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

No annotations are present, so the description carries full burden. It does not disclose data freshness, pagination, rate limits, or any side effects. The behavior is implied but not explicitly stated beyond fetching data.

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 sentence that is front-loaded and contains no fluff. It efficiently communicates the tool's purpose in minimal words.

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

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

For a tool with one parameter and no output schema or annotations, the description is adequate but minimal. It tells what the tool does but not what the response looks like or any constraints on the input.

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

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

Schema coverage is 100% and the single parameter 'ticker' is described as 'Stock ticker symbol'. The description adds no extra meaning beyond the schema, so 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 gets insider trading activity (Form 4 filings), which is specific and distinct from sibling tools that fetch financial statements or aggregate data. However, 'recent' is vague and could be more precise.

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 is provided on when to use this tool versus alternatives like search_filings or get_all_financial_data. There is no mention of prerequisites, exclusions, or typical 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, the description must disclose behavioral traits. It adds 'keyless' and the default series ID, but does not state what the response contains (e.g., time series values, units, date range, or pagination). This leaves significant ambiguity for a data 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 a single, front-loaded sentence with no waste. It efficiently conveys the core action, the keyless aspect, and the default parameter.

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 having only one parameter and no output schema, the description lacks critical context about the return format or the nature of the series data. It does not specify whether the tool returns annual, monthly, or current values, or any time-series metadata, making it incomplete for effective 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?

Schema description coverage is 100%, and the schema's parameter description already documents the default and meaning. The tool description repeats this without adding new syntax or format details, so it neither adds nor detracts from the schema baseline.

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

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

The description uses a specific verb ('Get') and clearly identifies the resource ('BLS labor market series'), with the notable default of the civilian unemployment rate. This distinguishes it from sibling tools that cover other data sources or metrics.

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 is provided on when to use this tool versus alternatives such as get_employment or get_inflation. The 'keyless' hint is a prerequisite, but there is no explicit context or exclusion criteria.

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 the description carries full burden. It only states what data is retrieved but offers no details on safety (e.g., read-only), authentication needs, rate limits, or any side effects. This is insufficient for a tool with no annotation coverage.

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?

Single sentence that conveys all necessary information. No wasted words, front-loaded with the tool's purpose.

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 parameterless tool, the description covers what data is retrieved and from where. However, it lacks details on output format or any caveats, which would be helpful given no output schema. Adequate but not fully 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?

Input schema is empty (0 parameters), so schema coverage is 100% trivially. The description adds value by naming the specific rates and data source, which is useful context beyond the empty schema. Baseline for 0 params is 4.

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

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

Clearly states the tool retrieves specific financial rates (Fed funds, mortgage, treasury yields) from FRED. The verb 'Get' and resource 'rates' are specific, and the description distinguishes it from sibling tools that handle other economic indicators.

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. However, the name and description make its purpose obvious, and siblings cover different data, so usage context is implied but not formalized.

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

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

No annotations are present, so the description must disclose behavioral traits. It only states the intended operation but does not mention side effects, authorization requirements, rate limits, or response format. For a read-like tool, it remains opaque.

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?

Single sentence, no unnecessary words, and the purpose is front-loaded. Every part 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?

The tool is simple (1 param, no output schema), but the description does not clarify what the output contains (e.g., segment names, revenue values, currency). This gap makes it less helpful than it could be.

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% (ticker with description). The description adds no additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves a revenue breakdown by product/service segments for a company, using a specific verb and resource. This distinguishes it from siblings like get_income_statement or get_all_financial_data.

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

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

No guidance on when to use this tool versus alternatives such as get_all_financial_data or get_income_statement. No context on prerequisites or typical 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?

No annotations are provided, so the description carries the full burden. It discloses the output type (positive/negative/neutral) but does not mention potential requirements like API keys, rate limits, or computational cost. The behavior is partially transparent.

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

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

The description is a single sentence with no wasted words. It is front-loaded with the main action and resource, making it easy to parse.

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 only one parameter and no output schema, the description is mostly complete. It specifies the input and the type of analysis. A minor gap is the lack of description of the output format (e.g., scores or labels), but it is not critical for this simple 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 schema already describes the ticker parameter as 'Stock ticker symbol' with 100% coverage. The description adds no additional meaning beyond mentioning 'a company', which aligns with 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 verb 'Get' and the resource 'sentiment analysis from SEC filings', specifying positive/negative/neutral signals from 10-K/10-Q reports. It is distinct from sibling tools like financial statements or company profile.

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 sentiment analysis from SEC filings but does not provide explicit guidance on when to use this tool versus alternatives, nor does it list when not to use it. No sibling comparisons are made.

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 trend data but lacks details on return structure, period count, data availability constraints, or any limitations, leaving the agent guessing about behavioral traits.

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

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

A single, well-structured sentence that front-loads the key action and content, with zero 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?

While the description is adequate for a simple one-parameter tool, it does not explain the return format or how it differs from sibling tools like get_all_financial_data that might also provide time-series data, leaving some contextual 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?

The input schema covers 100% of parameters with a description for ticker; the tool description adds no new semantics beyond what the schema already provides, thus baseline score applies.

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

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

The description uses a specific verb ('Get') and resource ('financial trends'), naming concrete metrics (revenue, profit, margins) and time dimension ('over time', 'across reporting periods'), clearly distinguishing from sibling tools like get_income_statement.

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 time-series financial data but does not explicitly state when to use this tool versus alternatives like get_all_financial_data or get_income_statement, nor does it provide exclusion criteria.

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 full responsibility for behavioral disclosure. It only mentions the data source (US Census Bureau), but does not specify whether the tool is read-only, if there are rate limits, data freshness, or any side effects. Basic info is present but insufficient for a data 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 a single sentence that efficiently communicates the core functionality. No wasteful or redundant phrasing, and it is front-loaded with the main action.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers the essentials. It could mention the expected output structure or typical response fields, but for a straightforward demographic lookup, it is largely complete.

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

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

The input schema has 100% description coverage for the single parameter 'zip_code', which is already well-described in the schema as '5-digit US ZIP code (e.g. '90210', '10001')'. The description adds no additional semantic context beyond what the schema provides, so 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 'Get demographics for a ZIP code' with specific categories (population, income, age, housing, education), using a specific verb and resource. It distinguishes from sibling tools like get_county_demographics by focusing on ZIP code.

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 vs alternatives such as get_county_demographics. The description does not state when it is appropriate or inappropriate to use, nor does it mention any prerequisites 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 carries the burden. It adds context about the source (openFDA public data) and the nature of the data, but doesn't disclose rate limits, pagination, error behavior, or what happens when no reports are found. It's minimally transparent but not fully.

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?

One sentence, front-loaded with the core purpose, and includes the key returned fields and source. No wasted words.

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

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

For a simple query tool, the description is fairly complete: it tells what it returns, the lookup key, and the data source. The lack of an output schema is mitigated by listing the fields. It doesn't mention limit default or pagination, but these are handled by the schema.

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

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

Schema coverage is 100%, so both parameters (drug and limit) already have descriptions. The description adds the context that the drug is searched by brand/generic name, but doesn't provide additional meaning beyond the schema. This matches the baseline for high 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 tool provides FAERS adverse event reports for a drug by brand or generic name, listing specific fields (report id, seriousness, reactions, outcomes). It differentiates from sibling tools like health_adverse_events_by_ndc by emphasizing the name-based lookup.

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 this tool should be used when you have a drug name (brand or generic), but it doesn't explicitly exclude by-NDC lookups or name alternative tools. There is no 'when not to use' guidance, making the usage context clear but not comprehensive.

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 adds context about the data source (openFDA FAERS) and public availability, and lists the returned content. However, it omits behavioral details such as rate limits, pagination, data freshness, or any caveats about the FAERS dataset, leaving room for more transparency.

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

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

The description is two sentences, front-loaded with the core purpose and followed by key details and source. Every sentence earns its place with no redundancy or filler.

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

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

For a simple two-parameter tool without an output schema, the description provides enough context: the data source, the NDC-based filtering, and the kind of results returned. It could be more complete by explicitly noting when not to use it or how results are paginated, but the core information is present.

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 full descriptions for both parameters (ndc and limit) with an example for ndc. The description adds no additional parameter-level detail, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool retrieves FAERS adverse event reports for a drug by NDC code, listing the specific data fields (report id, seriousness, reactions, outcomes) and source. This distinguishes it from sibling tools like health_adverse_events (which likely lacks NDC filtering) and health_drug_ndc (which is for drug lookup, not adverse events).

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 when an NDC code is available, but it does not explicitly state when to choose this tool over alternatives or mention exclusions. There is no comparison to siblings such as health_adverse_events, so the guidance is only implied, not fully articulated.

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 data source (openFDA, public data), the output fields, and the read-only lookup nature. It does not mention error handling or rate limits, but it provides meaningful behavioral context beyond the 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 a single, front-loaded sentence that conveys purpose, scope, and source without waste. Every phrase adds value.

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

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

For a simple single-parameter lookup with no output schema, the description is complete: it states the input (K number), the output fields, and the data source. No critical context is missing.

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

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

Schema description coverage is 100% (k_number has a description with an example). The tool description adds no new meaning about the parameter beyond restating 'by K number', so the baseline of 3 applies.

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 'Look up an FDA 510(k) premarket device clearance by K number' and lists the specific fields returned (device name, applicant, decision, decision date, product code). This is a specific verb+resource with a well-defined scope, and it differentiates from sibling health tools like health_drug_lookup or health_recalls.

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 the tool is used when you have a K number for a 510(k) device clearance, providing clear context. It does not explicitly mention when not to use it or name alternatives, but the specificity of 'by K number' offers sufficient guidance for selection.

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

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

No annotations are provided, so the description carries the full burden. It adds valuable context by citing the openFDA data source and public nature, but it does not mention rate limits, pagination, multiple-match behavior, or exact NDC formatting expectations. The read-only nature is implied but not explicitly stated.

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

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

The description is concise and front-loaded with the action and resource. The source and attribution are stated in a second sentence without redundancy or 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 simple lookup tool, the description provides a clear list of returned data fields, source, and attribution, which is adequate. However, it does not mention whether ambiguous names return multiple results or any output format, which would be useful given no output schema.

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

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

The input schema covers both parameters with clear descriptions, so baseline is 3. The description reinforces that the identifier can be an NDC or name but adds no additional syntax, type nuances, or examples beyond what the schema provides.

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 looks up an FDA drug label by NDC or brand/generic name, and lists the specific data fields returned. It distinguishes from sibling search tools by emphasizing exact identifier lookup, though it does not explicitly name alternatives.

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 implied usage is for retrieving a drug label using an exact NDC or name, but there is no explicit guidance on when to use this tool versus alternatives like health_drug_search or health_drug_ndc. The description gives context but no direct exclusions or comparisons.

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. It discloses the data source (openFDA, public data) and the return content, which is helpful. However, it does not mention any limitations, error behavior, or confirm read-only status beyond the verb 'look up'.

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 concise sentence, front-loaded with the core action and distinguishing detail (NDC). The source attribution is useful and not redundant. Every word adds value.

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

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

For a simple single-parameter lookup, the description covers what the tool does, the key output fields, and the data source. It is complete enough for an agent to invoke correctly, though it lacks details on return format or error handling, which are less critical given the 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?

The input schema already provides 100% coverage for the single parameter, including format examples (with/without dashes). The description adds no additional parameter information, so the baseline of 3 is appropriate.

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

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

The description states a specific verb+resource: 'Look up an FDA drug label by National Drug Code (NDC)' and lists the expected content (active ingredients, purpose, indications, warnings, manufacturer). This clearly differentiates it from generic search tools like health_drug_search or fda_drug_labels, which are not NDC-specific.

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

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

The description implies usage when an NDC code is available, but it does not explicitly mention when to avoid this tool or compare with sibling tools such as health_drug_search or health_drug_lookup. Context is clear enough, but exclusions are not stated.

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 states the source (openFDA, public data) and the returned fields, which gives useful context about data origin and output. It stops short of explaining error handling or limitations, but for a simple search tool this is adequate.

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

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

The description is a single, front-loaded sentence: it starts with the action (search), specifies the resource (FDA drug labels), then lists return fields and source. Every phrase adds value, and there is 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 simple search tool with two parameters, the description provides sufficient context about what can be searched and what is returned. It does not explicitly describe the response structure (e.g., list vs. object), but since there is no output schema, the listed return fields mitigate that gap. The overall picture is clear enough for an agent to use the tool effectively.

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

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

The schema description coverage is 100%, meaning both 'q' and 'limit' are already fully documented. The description adds no new parameter-specific details beyond what the schema provides, so the baseline of 3 applies.

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 FDA drug labels by brand name, generic name, or active ingredient, and lists specific return fields. This distinguishes it from sibling tools like fda_drug_labels and health_drug_lookup by focusing on label search with particular output fields.

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 when one needs to search drug label information by name, but it does not explicitly mention alternatives or exclusions. Similar FDA-related sibling tools exist, so clearer guidance on when to use this tool vs. others would improve the 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 for behavioral disclosure. It adds context about the source (openFDA, U.S. FDA), public data availability, the return fields (product, reason, classification, status, company), and the no-query behavior. It does not explicitly state it is read-only, but that is implicit from the domain.

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 long, front-loaded with the core purpose, and includes no filler. It efficiently communicates the action, scope, search behavior, return fields, and data source without 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 simple tool with two fully described optional parameters and no output schema, the description is sufficiently complete. It covers the purpose, source, return fields, and behavior with or without a query. It could mention pagination or temporal scope, but the core context is well covered.

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?

Both parameters (limit and product) are fully described in the input schema, including defaults, ranges, and examples. The description adds no additional parameter semantics beyond what the schema already provides, so a 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 provides FDA enforcement recalls for drugs, devices, and food, and explains how to search by product keyword or get the most recent when no query. It is specific about the resource and scope, though it does not explicitly distinguish itself from sibling tools like fda_recalls or health_recalls_by_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 by describing behavior with and without a query, but it does not provide explicit exclusions or alternatives among the many similar health/FDA sibling tools. It gives context for when to use it but not for when to avoid 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 carries full responsibility for behavioral disclosure. It states the source (openFDA, public data) and the output fields, but does not explain how company matching works (exact vs fuzzy), what happens with no results, or any rate limits or read-only nature. This is a significant gap for a tool with no annotation safety profile.

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 sentence with no filler. It front-loads the core purpose and includes the source. The list of output fields is useful but slightly adds length, yet still earns a 4 for efficiency.

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 (2 parameters, no output schema), the description is reasonably complete. It covers the purpose, the company parameter, and the return fields. It could benefit from a pointer to sibling health_recalls for broader context, but that is a usage guideline concern, not a completeness gap for this tool.

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

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

The input schema has 100% coverage for both parameters (company and limit), so the schema already provides the necessary semantics. The description adds minimal extra meaning—only the word 'specific' for company—and does not elaborate on the limit parameter. Baseline 3 is appropriate when the schema handles the 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 clearly states the tool lists FDA enforcement recalls for a specific recalling firm/company, and lists the output focus (product, reason, classification, status). This verb+resource+scope combination distinguishes it from sibling tools like health_recalls, which is a general recall lookup.

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

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

The phrase 'for a specific recalling firm/company' clearly implies when to use this tool (when you have a known company) versus a general recall search. However, it does not explicitly name alternative tools or state when not to use it, so it lacks the explicit exclusion that would push it to a 5.

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 source (openFDA), public data availability, and that sample counts are 'live'. It does not mention rate limits, response format, or caveats, but for a simple summary tool this is adequate though not rich.

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

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

The description is a single, front-loaded sentence stating the purpose first ('Health data API overview') and efficiently lists specifics via an em-dash. Every word earns its place, 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?

Given no parameters and no output schema, the description is largely complete: it names all three components (endpoint list, most recent recall, sample counts) and the data source. A minor gap is lack of detail on how the endpoint list is presented, but this is not critical for such a simple tool.

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

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

The tool has zero parameters, so the schema trivially covers 100% and there is nothing to document. The description adds context about what the tool returns, which is helpful but not directly about parameter semantics. Baseline 4 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 provides an 'overview' of the health data API, listing endpoints, the most recent FDA recall, and live sample counts. This distinguishes it from sibling tools like fda_recalls or health_adverse_events that retrieve specific data.

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

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

The description implies usage for getting a high-level overview of the FDA health API, and its content list ('endpoint list, most recent FDA recall, live sample counts') provides clear context. However, it does not explicitly state when to prefer this over siblings or any exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the source and that no API key is needed, but it does not describe the response format, potential rate limits, or whether the operation is read-only. This is insufficient for a tool with no annotation coverage.

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 sentence followed by a brief source note. It is front-loaded with the action and resource, contains no redundant words, and every part adds value.

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

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

The tool is relatively simple with two parameters, and the description provides the key context of the IMF source and licensing. However, without an output schema, it does not explain the return structure or any potential edge cases, leaving some gap in completeness.

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

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

Schema description coverage is 100%, with both parameters already having descriptive text and indicator codes listed in the schema. The description adds a few example types (debt, etc.) but does not add significant meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool gets an IMF World Economic Outlook indicator for a country, listing examples. It distinguishes from siblings by naming IMF as the source, though it does not explicitly name alternatives like Eurostat or OECD.

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 context by identifying the IMF as the data source, making it clear for IMF-specific data. However, it does not explicitly state when to use this tool versus other economic indicator tools or provide any exclusions.

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 does not disclose which metrics are compared, how averages are computed, or any potential side effects. Vague 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?

Single sentence, minimal waste, clear and direct. Appropriate length for the tool's simplicity.

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?

No output schema and lacks description of return format, specific metrics, or limitations. Agent cannot predict what output to expect, making it incomplete.

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 (ticker) with full schema coverage. Description adds context (compare against industry) but schema already explains ticker. Baseline 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?

Description clearly states the action (compare), subject (company's financial metrics), and reference (industry averages). Distinct from siblings like get_all_financial_data and compare_companies.

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 vs alternatives, no prerequisites, when-not-to-use, or context. Agent must infer from sibling names.

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 the data source ('ProPublica Nonprofit Explorer'), the scope ('latest 990/990-EZ/990-PF filing, plus 5-year filing history'), and the specific data elements returned. This goes beyond a bare lookup and gives useful context, though it does not mention potential edge cases such as missing filings or data freshness.

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, front-loaded sentences. The first sentence clearly states the purpose and the data fields, and the second provides the source. No redundant or filler language; every word 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?

Given the low complexity (one parameter, no output schema), the description is complete. It lists the return values (revenue, expenses, etc.) and the history scope (5-year filing history), which is sufficient for a lookup tool. It doesn't describe the exact JSON structure, but that is not necessary given the context.

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 covers 100% of the single parameter ('ein') with a detailed description including format and examples. The tool description only mentions 'by EIN' without adding any semantic detail beyond the schema. Baseline of 3 is appropriate since the schema does the heavy lifting.

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 function with a specific verb ('Get') and resource ('IRS Form 990 financial data for a US nonprofit by EIN'). It enumerates specific data fields (revenue, expenses, assets, liabilities, officer compensation, etc.) and distinguishes itself from sibling tools by explicitly targeting nonprofit tax filings, not corporate financials.

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 implies when to use this tool: when you need IRS Form 990 data for a US nonprofit identified by EIN. It does not explicitly mention alternatives or exclusions (e.g., 'not for for-profit companies'), but the qualifier 'US nonprofit' makes the intended use case unambiguous. More explicit guidance on when not to use it would elevate this to a 5.

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 that the operation is a read (Get), specifies the data source (NOAA), and notes it is free for any purpose. It does not detail response structure or error handling, but for a simple read-only tool this is reasonably transparent.

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

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

The description is two sentences, front-loaded with the primary function, and avoids redundancy. The extra note about the source is useful and does not waste space.

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

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

For a simple one-parameter tool with no output schema, the description is adequately complete: it states what it does, the geographic scope, and the data source. It could mention the shape of the response, but the core behavior is clear without it.

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

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

Schema coverage is 100% because the only parameter 'state' has a clear description. The tool description adds little beyond repeating the state code format, so it does not significantly enhance the parameter semantics.

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

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

The description clearly specifies the verb 'Get' with the resource 'active NWS weather alerts' and the scope 'for a US state'. It enumerates alert types (watches, warnings, advisories) and provides examples, making it distinct from siblings like nws_forecast.

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 the context for use (retrieving active weather alerts for a state) and even mentions the data source and free nature, but it does not explicitly exclude alternatives or state when not to use it relative to sibling tools.

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

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

With no annotations provided, the description carries the full burden. It adds useful context: it is official NWS data, free for any purpose, and returns 7-day periods with specific elements. However, it omits important behavioral details such as US-only coverage, output format (JSON), coordinate bounds, and potential error cases. This is a moderate disclosure.

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

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

The description is a single, front-loaded sentence that captures the core purpose and key output details, followed by a brief source note. No wasted words; all information is relevant.

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 low complexity (2 params) and absence of an output schema, the description provides a good overview of what is returned (periods, temperature, wind, short/detailed forecast) and the source. However, it misses the US-only limitation and does not describe the exact response structure, leaving minor gaps for an agent.

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

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

Schema coverage is 100% (lat and lon are fully described), so the description adds little beyond what the schema already shows. It merely refers to latitude/longitude as the input, which is already in the property descriptions. The baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Get') and a clear resource ('official NWS weather forecast'), and explicitly states the output content (7-day periods, temperature, wind, short/detailed forecast) and input (latitude/longitude). It is distinct from sibling tools like nws_alerts and other data-heavy 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 clearly implies use when you need a weather forecast for a given latitude/longitude. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough to differentiate from nws_alerts (which is for alerts) and other financial/demographic tools.

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

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

With no annotations, the description carries the transparency burden. It discloses useful behavior: the indicator is amplitude-adjusted, sourced from OECD Data Explorer, openly reusable with attribution, and requires no key. However, it does not describe the return format (e.g., time series structure, units, historical depth) or potential limitations, leaving some ambiguity for an agent.

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, dense sentence that front-loads the core purpose ('Get OECD Composite Leading Indicators') and efficiently includes key details: amplitude-adjusted, example countries, source, and licensing. No unnecessary words or repetition; every phrase 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?

The tool is simple (one optional parameter, no output schema), and the description covers purpose, scope, source, and authentication. However, since there is no output schema, the description should ideally explain what the response contains (e.g., time series of CLI values) to be fully complete; this is missing, leaving a gap.

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

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

The schema has 100% coverage for the single 'country' parameter, so the schema already documents it. The description's mention of 'OECD country (USA, DEU, JPN, GBR, etc.)' mirrors the schema's default and examples, adding no new meaning. The baseline of 3 applies because schema coverage is high.

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

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

The description uses a specific verb ('Get') and identifies a precise resource ('OECD Composite Leading Indicators (CLI, amplitude-adjusted)') plus the target scope ('for an OECD country'). This clearly distinguishes it from sibling economic tools like eurostat_gdp or get_gdp, which target different data providers or indicators.

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 by stating it is for OECD CLI data, but it does not explicitly state when to choose this tool over alternatives (e.g., Eurostat, IMF) or when not to use it. The mention of 'no key' and the source provides some practical context, but no exclusions or alternative recommendations are given.

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, and the description does not disclose any behavioral traits such as rate limits, side effects, or safety. Only states what it does, not how it behaves.

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

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

Single sentence with 10 words, no redundancy. Efficiently communicates purpose.

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?

Lacks explanation of return format or default behavior for optional parameters. For a screening tool with no output schema, more detail on what is returned would improve completeness.

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

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

Schema covers both parameters with descriptions (100% coverage). The description adds example criteria but does not clarify how 'screen' parameter values relate to the listed criteria, providing minimal added value.

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

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

Description clearly states it screens companies by financial criteria and lists examples. However, it does not directly map to the 'screen' parameter values (growth, value, quality, momentum), causing slight ambiguity.

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 vs. siblings like batch_query or get_company_health. No mention of prerequisites or exclusion criteria.

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 fully disclose behavioral traits. It lacks details on whether the operation is read-only, pagination behavior (though limit parameter suggests it), error handling, or safety implications. The description only hints at the search nature without deeper transparency.

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

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

The description is short with two sentences, but the first sentence is a run-on that conflates multiple search facets. It is concise but somewhat incomplete and misleading, reducing effectiveness.

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 no output schema and moderate parameter count (3). The description does not explain what the output contains (e.g., filing metadata, links) or how to interpret results. It omits important context like default limit behavior and missing parameters.

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

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

The input schema has 100% coverage with descriptions, so baseline is 3. However, the description adds claims about date range and keyword filtering that are not reflected in the parameters, confusing the agent. This introduces noise rather than value.

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

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

The description states it searches filings for a company and mentions form type, date range, and keywords. However, the input schema only has form_type and no date range or keyword parameters, creating a mismatch. This detracts from clear communication of the tool's actual capabilities.

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 suggests using get_filing_types first to see available form types, which is helpful guidance. However, it does not provide when-not-to-use instructions or differentiate from other search-oriented sibling tools like batch_query or get_all_financial_data.

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 must carry the behavioral disclosure burden. It does add useful context such as 'keyless' (no auth needed) and lists output fields, which helps set expectations. Yet it omits details like query optionality, pagination, or potential result limits, 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 a single, front-loaded sentence that communicates the tool's purpose, data source, authentication mode, and key output fields with no redundant words. It uses a dash to efficiently append relevant details.

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 single-parameter tool with no output schema, the description adequately covers the core aspects: what it searches, where from, and what fields are returned. It falls slightly short of full completeness by not noting that the query is optional or what happens with no query, but the tool's simplicity makes these gaps acceptable.

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% because the single parameter 'query' already has a descriptive comment with an example. The description adds no additional meaning about the parameter itself, only mentions output fields. This matches the baseline for high 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 a specific verb ('Search') and resource ('scholarly research works'), and adds the source (OpenAlex) and keyness (keyless) to distinguish it from other search tools. It also lists the key result fields (titles, authors, year, citation counts), making the purpose unmistakable.

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 scholarly research by naming the resource type, which provides clear context for when to use this tool. However, it does not explicitly state when not to use it or mention alternative tools, so it stops short of full exclusion guidance.

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

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

With no annotations, the description carries the burden. It discloses the data source (U.S. Treasury Fiscal Data), public domain status, and that no API key is needed. However, it doesn't mention return format, pagination, data freshness, or any limitations beyond the parameter 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 a single concise sentence that effectively communicates the tool's purpose and key data fields, followed by a brief source note. No wasted words.

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

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

For a simple tool with one optional parameter and no output schema, the description adequately covers the data content and source. It could mention sort order or the meaning of 'recent', but the schema covers the default limit, making it largely 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 parameter 'limit' is fully documented in the schema. The description adds no additional param semantics, which is acceptable per baseline for high coverage.

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

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

The description clearly states the tool gets recent completed Treasury auction results, listing specific fields (yield, bid-to-cover, CUSIP, maturity). This specific verb+resource combination distinguishes it from sibling treasury tools like treasury_debt or treasury_interest_rates.

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. The description implies it is for auction results, but it doesn't mention exclusions or alternatives, leaving the agent to infer usage from the sibling list.

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 burden. It discloses the data source (US Treasury Fiscal Data) and licensing (free, unrestricted, commercial use), adding context beyond the schema. It does not mention update cadence or return format, but the 'Get' verb and component list imply a read-only data retrieval with no notable 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 concise sentences, front-loaded with the core purpose, and every phrase adds information: tool purpose, data components, source, licensing. 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 parameterless tool, the description is largely complete: it names all return components, the source, and licensing. It lacks a note about time-sensitivity (e.g., as-of date) but this is a minor gap given no output schema and 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?

Zero parameters exist in the schema, and baseline for no-param tools is 4. The description adds no parameter syntax, but none is needed; it instead explains exactly which debt figures are included.

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 opens with specific verb 'Get' and resource 'US federal debt', then enumerates the debt components (total public debt, debt held by public, intragovernmental holdings). This clearly differentiates from sibling tools like treasury_interest_rates or treasury_fx_rates by naming the domain and scope.

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 when-to-use vs alternatives is stated, but the domain (US federal debt) and source are clear, making it easy for an agent to infer relevance. The description does not name sibling exclusions, so guidance is implied 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?

With no annotations, the description carries the transparency burden. It discloses that data is 'daily', covers '100+ currencies', and is sourced from US Treasury Fiscal Data, described as 'free, unrestricted'. This gives useful behavioral context without overpromising.

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

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

The description is two sentences, front-loaded with the main verb and resource, and provides essential source information without fluff. Every word contributes value.

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

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

Given no output schema and no parameters, the description adequately explains the tool's output ('daily official FX rates for 100+ currencies') and source. It lacks some detail on output format (e.g., base currency, date handling) but is sufficient for a simple read-only 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?

The input schema has zero parameters, so description can't add parameter details. However, it adds context about what the returned rates cover (official US Treasury rates for many currencies). Given the schema already shows no parameters, the description adequately complements it.

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: 'Get official US Treasury reporting rates of exchange' with scope 'daily official FX rates for 100+ currencies.' It uses a specific verb and resource, and the mention of 'US Treasury' distinguishes it from general FX tools like fx_get_rates.

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 clear context: use this tool when you need official US Treasury FX rates, specifically daily rates for 100+ currencies. It implies a distinct use case from sibling FX tools but does not explicitly name alternatives or exclusions.

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 carry the full transparency burden. It reveals important behavioral traits: the data is public domain, requires no API key, and updates monthly. This goes beyond a simple 'get' but does not disclose pagination, response format, or potential rate limits. For a simple read-only tool, this is adequate but not rich.

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 fluff. The first sentence states the action and result; the second provides source and access details. Every word earns its place.

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

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

For a parameterless read-only tool with no output schema, the description is sufficiently complete. It explains what data will be returned (fine troy ounces, book value, by location), the source, and the update frequency. An agent can select and invoke this tool with confidence.

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

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

The tool has zero parameters, so the baseline score is 4. The description mentions 'by location' as an output grouping concept, not a parameter, and does not need to add parameter-level detail since none exist.

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

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

The description uses a specific verb ('Get') with a well-defined resource ('U.S. gold reserve holdings') and includes specific content details ('fine troy ounces and book value by location'). It clearly distinguishes itself from other treasury tools that focus on debt, interest rates, or auctions.

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 clear context: it specifies the data source (U.S. Treasury Fiscal Data) and update frequency (monthly), which helps an agent know when to use it. It also notes 'no key' and 'public domain,' making access requirements explicit. However, it does not explicitly exclude alternatives or contrast with sibling treasury tools.

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

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

Since annotations are absent, the description must convey behavioral traits. It discloses the source (U.S. Treasury Fiscal Data), public domain status, and no key required, which is valuable for the agent. The verb 'Get' implies read-only with no side effects. However, it doesn't mention the return format or whether data is historical/current, leaving some gaps.

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?

Exactly one sentence, front-loaded with action and resource. Includes essential source and access details 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?

This is a low-complexity, zero-parameter tool with no output schema. The description covers what, frequency, source, and access. It could mention the return types explicitly, but for a simple public data fetch, it is adequate.

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

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

The tool has zero parameters, so the schema provides no parameter details. The description mentions 'by type (monthly)' but this is a data characteristic, not a parameter. The description doesn't mislead; with an empty schema, the agent understands there are no arguments. Baseline 4 for zero params applies.

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 verb 'Get' plus specific resource 'average interest rates on U.S. Treasury securities' clearly states what the tool does. The frequency 'monthly' and source distinguish it from sibling treasury tools like auctions and debt.

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

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

The description gives clear context for when to use: when Treasury interest rate data is needed. It does not explicitly mention alternatives or exclusions, but the specificity of the tool makes its use case obvious. No direct comparison to 'get_rates' or other financial tools, so not a 5.

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 present, the description carries the full burden of behavioral disclosure. It does add useful context about the data source (public domain, no key required), but it does not address potential behaviors such as update frequency, error responses, or missing data. The description is not contradictory.

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 sentence that front-loads the action and resource, then lists the data contents and source. Every segment adds value, with no redundant or filler 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?

Given the tool's low complexity (no parameters, no annotations, no output schema), the description is fully adequate. It specifies the data categories returned and the source, and the absence of further detail does not create ambiguity.

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

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

The tool has zero parameters, so per the baseline, the description is not required to elaborate on parameter formatting or meaning. The description correctly avoids parameter details since none exist, and the baseline of 4 applies.

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

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

The description uses a specific verb ('Get') and a precise resource ('the latest Monthly Treasury Statement'), then enumerates the exact contents (receipts, outlays, surplus/deficit, top agencies by outlays). This clearly distinguishes it from sibling tools like treasury_debt or treasury_summary.

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

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

The description gives clear context about what the tool returns but does not explicitly state when to use it versus alternatives like treasury_debt or treasury_interest_rates. There is no mention of exclusions or when this tool would be preferable, leaving the guidance implied 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?

With no annotations, the description carries the full transparency burden. It discloses that the data is sourced from U.S. Treasury Fiscal Data, is public domain, and requires no API key, which is useful operational context. However, it does not describe the response format or potential caveats like latency or error handling, but for a simple zero-parameter fetch, the provided information is reasonably transparent.

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

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

The description is a single, front-loaded sentence that immediately states purpose and key content. The additional source/authentication note is necessary for usage. No word is wasted.

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 (no parameters, no output schema), the description fully specifies what the tool returns: a dashboard with the listed treasury indicators. The source note also covers data origin and key requirements, making the description sufficient for an agent to select and invoke the tool.

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

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

The tool has zero parameters, and the schema is empty with 100% description coverage (nothing to describe). Per the rubric, a baseline of 4 is appropriate since there is no parameter ambiguity to resolve.

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 'Get' and the resource 'headline U.S. Treasury dashboard', enumerating specific components (national debt, interest rates, monthly statement, gold reserve, EUR rate). This distinguishes it from sibling tools like treasury_debt or treasury_interest_rates by presenting it as a cohesive summary rather than a single metric.

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

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

The phrase 'headline dashboard' conveys a clear use case for obtaining a quick overview of U.S. Treasury data. It implies a high-level summary, making it distinct from the more granular sibling tools, though it does not explicitly name alternatives or state 'use this when you need a broad snapshot'.

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 convey behavioral traits. It discloses the source as 'Companies House (UK)', implying official data, and the verb 'Get' indicates a read-only operation. However, it doesn't mention potential issues like data freshness, pagination, or error behavior, so it's adequate but not rich.

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

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

The description is two sentences, front-loaded with the core function, and every word contributes value.

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

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

For a simple one-parameter tool with no output schema, the description provides essential context: what it does, source, and use case. It lacks explicit details about return format, but that is less critical given the simplicity. It is complete enough.

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

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

The input schema fully describes the 'number' parameter with an example, and the description doesn't add additional parameter-level information. Since schema coverage is 100%, a baseline of 3 is appropriate.

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

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

The description uses the specific verb 'Get' and clearly identifies the resource as 'registered charges (mortgages/debentures) for a UK company'. It distinguishes from sibling tools by focusing on charges, and provides the data source.

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

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

The description states 'Best for credit risk assessment', giving clear context for when to use it. However, it doesn't explicitly describe alternatives or exclusions, so it falls short of a 5.

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 burden. It adds useful behavioral traits: results are sorted most recent first, and it covers specific filing categories. However, it doesn't mention default limit behavior or potential errors, though 'Get' reasonably implies a read-only operation.

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

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

The description is a single, front-loaded sentence with no wasted words. It efficiently communicates purpose, content, ordering, and source.

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 two-parameter tool, the description is sufficiently complete. It covers purpose, content, and ordering, but since there is no output schema, a bit more detail about the response structure could be helpful. Still, it is adequate for understanding and invoking the tool.

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

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

The schema already describes both parameters fully (limit with default, number with example). The description adds no additional parameter details, which is acceptable since schema coverage is 100%.

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 UK company filing history, lists example filing types (accounts, confirmation statements, insolvency events), specifies ordering (most recent first), and identifies the data source. This distinguishes it from sibling tools like uk_company_officers or uk_company_charges.

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 clear context: it's for a specific company's filings, not a general search (unlike search_filings). It doesn't explicitly state when not to use it or name alternatives, but the focused scope and source make the intended use obvious.

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 burden of behavioral disclosure. It reveals that the grade is 'derived' and 'heuristic', which indicates an approximate calculation, and adds 'not financial advice' as a disclaimer. This goes beyond just the function name, though it doesn't detail the exact heuristic or return format beyond the grade.

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

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

The description is two sentences, front-loaded with the primary action, and every clause adds value: the purpose, the differentiating factor, and the heuristic disclaimer. No redundant words.

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

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

For a simple tool with one parameter and no output schema, the description provides adequate context: what it returns (A-E grade), how it's derived (from status, charges, filings), and its limitations (heuristic, not financial advice). It could mention the output structure more explicitly but remains reasonably complete.

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

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

The input schema already has 100% coverage for the single parameter 'number' with an example. The description doesn't add any extra parameter semantics, which is acceptable given the schema's completeness. Baseline 3 applies.

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 specific function: 'Get a derived company health grade (A-E) for a UK company from status, charges, and filing signals.' It uses an action verb and resource, and explicitly differentiates itself from raw register data, distinguishing it from sibling tools like uk_company_profile or uk_company_charges.

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

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

The phrase 'Differentiator vs raw register' tells the user when to use this tool (for a derived grade) versus alternatives that provide raw data. The caveat 'Heuristic, not financial advice' also guides appropriate usage. However, it doesn't explicitly name alternative tools or provide exclusion criteria, so it's clear but not exhaustive.

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 carry the burden of behavioral disclosure. It adds source attribution (Companies House, OGL v3.0) and mentions appointment dates, which gives some insight into the data content. However, it does not disclose potential limitations like pagination, data coverage, or behavior for invalid company numbers.

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 redundant information. It front-loads the core purpose and follows with a concise use case and source note, making it highly 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 simple one-parameter tool with no output schema, the description gives enough context to understand what is returned (directors, officers, appointment dates) and when to use it. It could be slightly more explicit about the response structure, but it is adequate for the complexity.

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

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

The input schema already provides a full description of the parameter 'number' with an example, and the schema coverage is 100%. The tool description adds no additional information about the parameter, so the score stays at the baseline for high 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 tool gets UK company directors and officers with appointment dates, using a specific verb and resource. It distinguishes from siblings like uk_officer_search by focusing on a specific company's officers rather than a general 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?

The description provides a clear use case ('Best for KYB director checks') and identifies the data source, which gives context for when to use it. However, it does not explicitly mention when not to use it or name alternatives such as uk_officer_search.

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