filingapi

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It clearly states what data is returned (GICS sector/industry, counts of filings/transcripts), which is meaningful for an agent estimating impact. It does not cover error cases or data sources, but that is acceptable for a simple read-only getter.

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 the primary purpose front-loaded. No filler or redundant words; every sentence contributes meaningful specification of the tool's output.

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

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

Given the tool's simplicity (one parameter, no output schema), the description provides a sufficient overview of the returned data. It could mention return format or edge cases, but the core functionality is well covered for an agent deciding whether to invoke it.

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

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

Schema description coverage is 0%, and the description does not explain the 'ticker' parameter beyond what the schema already states. The parameter is a single, self-evident stock symbol, so it is not a severe gap, but the description adds no value in clarifying format or constraints.

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

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

The description states the specific verb 'Get' and resource 'company info' with concrete coverage: sector, industry, and data coverage. It clearly distinguishes itself from sibling tools like get_filings or get_transcript by focusing on company-level metadata and counts.

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 is for retrieving company classification and filing/transcript counts, but it gives no explicit guidance on when to use it versus alternatives or any context boundaries. No exclusionary language or sibling comparisons are present.

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 a key behavioral trait: 'Defaults to the next 7 days if no dates specified.' It also specifies the output content (dates, EPS, revenue estimates). For a read-only 'get' tool, this is adequate transparency, though it doesn't detail edge cases like empty results or date format 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 concise and front-loaded. The main purpose is stated in the first sentence, the default behavior in the second, and usage guidance in the third. No redundant information is present; every sentence adds value.

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

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

The tool is simple (3 optional params, no output schema), and the description covers its core behavior and default. However, the complete absence of parameter semantics (0% schema coverage) and lack of output structure details leave gaps. For instance, agents don't know the expected date format or whether results are paginated. Thus it is minimally complete but with clear 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 schema has 0% description coverage, so the description must compensate. It only hints at parameters by saying 'if no dates specified' and 'specific company's next earnings date is,' implying ticker and date range usage. It does not explain date format, whether dates are inclusive, or how ticker should be formatted. This is insufficient for agents to construct parameters correctly without additional assumptions.

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: 'Get upcoming earnings announcement dates with EPS and revenue estimates.' This is a specific verb+resource combination that distinguishes it from sibling tools like get_guidance or get_transcript, which focus on other financial data. The mention of default date range adds clarity.

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 provides usage scenarios: 'Use this when asked about upcoming earnings, who reports this week, or when a specific company's next earnings date is.' This gives clear context for when to invoke the tool. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks 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 disclosing behavioral traits. It only states that it gets 'full detail' without describing what that entails (e.g., response format, size, pagination, rate limits, or permissions). The description adds no safety or side-effect context beyond the implied read-only nature of 'get.'

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 redundancy. The first sentence leads with the core function and primary parameter, and the second provides workflow guidance. Every word contributes to understanding the tool's purpose and usage.

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 there is no output schema, the description should hint at the return value. 'Full detail' is vague and could mean anything from metadata to the entire filing text. While the usage guidance is helpful, the lack of return structure or additional behavioral context makes the description incomplete for a tool with no output schema or 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?

Schema coverage is 0%, so the description must compensate. It explains the accession number with an example and context ('unique SEC filing ID'), which adds meaning. However, the ticker parameter is only mentioned by name without any format or clarification. The description partially bridges the schema gap but leaves one parameter semantically thin.

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 the full detail of a single SEC filing using ticker and accession number. It explicitly names the resource (SEC filing) and the unique identifier format, distinguishing it from sibling tools like get_filings which list filings. The verb 'get' and the scope 'single filing' make the purpose unambiguous.

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

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

The description provides explicit usage guidance: 'Use this after get_filings or get_guidance has identified a specific filing you want to inspect in full.' This defines when to use the tool relative to alternatives, naming the predecessor tools and clarifying the workflow. It avoids ambiguity about when this tool should be invoked.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns dates and accession numbers, and that it does not include guidance extraction. However, it does not mention other behavioral traits such as whether all filings or only recent ones are returned, pagination, rate limits, or any authentication requirements. The provided information is useful but not comprehensive.

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

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

The description is two sentences, front-loaded with the main purpose, and uses clear, concise language. The additional mention of the limitation and alternative tool is placed at the end without unnecessary filler. Every sentence 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 only two parameters and no output schema, the description covers the core purpose, expected output fields (dates, accession numbers), and a key exclusions. It lacks details on return format (e.g., list vs. single object) and error handling, but these are partly inferable. Given the simplicity of the tool, 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?

The schema has no descriptions (0% coverage), so the description must compensate. It mentions 'by ticker symbol' and lists example filing types (8-K, 10-Q, etc.), which helps interpret the ticker and filing_type parameters. However, it does not explain the valid values for filing_type (beyond examples) or the default behavior when filing_type is null. This is partial compensation but not complete.

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: 'Get SEC filings (8-K, 10-Q, etc.) for a company by ticker symbol.' It uses a specific verb and resource, and explicitly differentiates from get_guidance by noting it does not include guidance extraction. This makes the purpose unambiguous and distinguishes it from at least one sibling.

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

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

The description provides explicit usage context: 'Use this to see what filings are available for a company, with dates and accession numbers.' It also states a clear exclusion and alternative: 'Does not include guidance extraction - use get_guidance for that.' This gives the agent both when-to-use and when-not-to-use guidance, with a specific alternative tool.

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

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

With no annotations provided, the description carries the full burden. It discloses that results are keyword-matched sentences and may include both forward guidance and recent reported results, warning the user to read carefully. This is meaningful behavioral context beyond the schema, though it doesn't describe return format or potential limitations like date ranges.

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

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

The description is concise and well-structured. The first sentence states the core purpose, the second provides usage context, and the note adds a critical caveat. Every sentence serves a clear function, 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?

For a simple tool with one parameter and no output schema, the description is complete. It explains what the tool does, when to use it, what the output looks like (`keyword-matched sentences`), and a significant caveat about historical vs. forward-looking content. This is sufficient for an agent to select and invoke 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?

The schema has only one parameter, `ticker`, with 0% description coverage. The description compensates by stating `by ticker symbol` in the first sentence, making it clear the input is a stock ticker. Though it doesn't specify format (e.g., uppercase), the single parameter is self-explanatory and the mention adds value.

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

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

The description clearly states the tool extracts forward-looking guidance language from SEC filings by ticker symbol. This is a specific verb+resource+scope that distinguishes it from sibling tools like get_filings or get_transcript, which focus on raw filings or transcripts.

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

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

The description provides explicit guidance on when to use the tool (`Use this when asked what a company said about future performance, revenue outlook, margin expectations, or other forward-looking statements`). However, it does not explicitly name alternatives or state when not to use it, though the context is clear enough for most agent decisions.

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 behavioral context. It discloses case-insensitive sector matching and lists valid event_type values, which adds useful behavioral detail. However, it doesn't define 'recent' (time window), mention pagination, or describe the output format, 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 concise, with four short sentences each adding distinct value: purpose, case-insensitivity, prerequisite usage, and optional filter. No wasted words 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?

For a simple two-parameter tool with no output schema or annotations, the description covers purpose, parameter semantics, and usage prerequisite. The main gap is the unspecified 'recent' time frame and lack of return format details, but overall 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 coverage is 0%, but the description fully explains both parameters: sector with case-insensitive examples and guidance to use list_sectors, and event_type with a comprehensive list of allowed values. This adds meaning well beyond the bare schema types.

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 SEC filings for all companies in a given sector, with a specific verb and resource. This distinguishes it from siblings like get_filings (likely company-specific), search_filings (search-based), and list_sectors (sector 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?

It advises using list_sectors first if unsure of sector names, providing a clear prerequisite. It doesn't explicitly exclude alternatives like get_filings for individual companies, but the scope is clearly sector-based, making the usage context 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 carries the full burden. It discloses the data source (SEC 8-K filings), generation model (Claude Haiku), timing (within minutes of EDGAR publication), and the structure of each signal. It also explains the default time window and how to modify it via hours. It doesn't reveal potential limitations like rate limits or pagination, but it provides meaningful behavioral context beyond a simple 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 about 60 words across four sentences. It front-loads the purpose, then adds signal contents, use case, and time-window guidance. No fluff; each sentence serves a 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?

The tool is simple (2 optional params, no output schema). The description covers the main purpose, output shape, use case, and parameter adjustment. It could explicitly mention 'limit' behavior, but overall it's sufficient for an agent to invoke 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?

The schema has no descriptions, so the description must compensate. It explains the 'hours' parameter: 'Default window is 30 days; set hours=24 for today only.' However, it does not explicitly explain the 'limit' parameter, though 'limit' suggests a cap on returned signals. Given 0% schema coverage, the description partially mitigates but leaves limit undefined.

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+resource: 'Get real-time AI sentiment signals derived from SEC 8-K filings.' It further details the signal contents (ticker, classification, confidence score, summary, factors), distinguishing it from sibling tools about raw filings or earnings. This isn't a tautology and provides clear differentiation.

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

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

The description explicitly says when to use: 'Use this when asked which companies have recently filed bullish or bearish 8-Ks, or to screen for sentiment shifts across the S&P 500.' It also gives usage context for the hours parameter ('Default window is 30 days; set hours=24 for today only'). However, it doesn't explicitly mention alternatives or when not to use it, so 4.

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 transparency burden. It discloses key behaviors: returns a list rather than a single result, does not filter server-side, and specifies the fields included in each transcript. It does not mention authentication or error cases, but for a simple read-only GET tool, this is sufficient context.

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

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

The description is three sentences, each earning its place: purpose, return value structure, and usage guidance. It is front-loaded with the main action and contains no redundant or irrelevant information.

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

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

For a tool with no output schema and no annotations, the description adequately explains the return format (list with fields) and the non-filtering behavior. It could mention edge cases like no transcripts available, but the essential information for selecting and invoking the tool 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?

With only one parameter (ticker) and no schema descriptions, the description compensates by stating 'by ticker,' making the parameter's purpose clear. It does not specify format or case, but the ticker symbol is a standard identifier and no further elaboration is necessary.

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 all available earnings call transcripts for a company by ticker,' using a specific verb, resource, and scope. It distinguishes itself from siblings like get_filings and search_filings by explicitly mentioning earnings call transcripts and contrasting with written SEC filings.

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

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

The description explicitly says when to use this tool: 'Use this when asked what was said on an earnings call, as opposed to in written SEC filings.' It also advises the agent on how to handle quarter-specific requests by searching within the returned list, which provides clear usage context and differentiates from alternatives.

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

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

With no annotations, the description carries the transparency burden. It discloses output scope ('11 GICS sectors covering the S&P 500') and that each sector includes the number of tickers, which is meaningful behavioral context for a simple read-only list 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?

Two compact sentences, with the primary action first and the usage context immediately following. Every sentence adds value with no redundancy.

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

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

For a parameterless list tool with no output schema, the description sufficiently covers what is returned, how many sectors, and the coverage universe. No critical behavioral or output details are missing.

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

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

The tool has zero parameters, so no parameter documentation is needed; baseline 4 applies. The description adds context about what is returned rather than 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 opens with 'List all available sectors', which is a specific verb and resource. It further distinguishes from siblings like get_sector_filings by emphasizing the sector list itself rather than 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?

It gives explicit use cases ('asked about sectors, industries'), but also claims it helps 'find which sector a type of company falls under', which is misleading since this tool only lists sectors and cannot map a specific company to one. It does not mention get_company_info as a better alternative for company-specific sector lookups.

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 of behavioral disclosure. It mentions optional filters and date format, which is helpful. However, it does not state whether the operation is read-only, requires authentication, or describes the response format. It adds some behavioral context but lacks a full safety/return 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 concise and well-structured: two sentences, first states the core function and filters, second provides usage guidance. Every word earns its place, no redundant information.

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

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

The tool has 4 parameters, no output schema, and no annotations. The description covers the purpose, all parameters at a high level, and usage context. It doesn't mention return shape or limitations, but given the simple search nature, it is reasonably complete. Slightly more detail on output would elevate it further.

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

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

Schema description coverage is 0%, so the description must compensate. It explains that 'query' is a keyword, 'ticker' is an optional filter, and date range uses YYYY-MM-DD format. This adds meaning beyond the bare schema names, though it doesn't elaborate on date_from/date_to semantics beyond their names.

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 across all SEC filings in the database by keyword.' It uses a specific verb (search) and identifies the resource (SEC filings). It also distinguishes itself from siblings by explicitly noting it is for open-ended questions 'rather than looking up one known ticker.'

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

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

The description provides explicit usage guidance: 'Use this for open-ended questions like... rather than looking up one known ticker.' This gives a concrete example and indicates when not to use it, effectively guiding the agent toward the appropriate tool.

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