filingapi

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 the return fields (sector, industry, counts) but omits behavioral details such as read-only nature, potential errors, or performance implications. The description adds moderate context beyond schema but lacks completeness.

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 two clear sentences, no redundancy, and front-loaded key information. Every sentence contributes to understanding the tool's purpose and 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 adequately explains what it returns. However, it could mention limitations (e.g., requires valid ticker) or error handling. Overall sufficient for a straightforward lookup tool.

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

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

Schema description coverage is 0% (no descriptions in input schema), so the description must compensate. It mentions the ticker parameter implicitly but does not clarify expected format, validation rules, or constraints. This adds minimal semantic value 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 clearly states the tool retrieves sector, industry, and data coverage info (filings/transcript counts) for a company. It uses a specific verb 'Get' and identifies the resource, effectively distinguishing from sibling tools like get_filings or get_transcript.

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 a user needs company classification and coverage counts, but it offers no explicit guidance on when to prefer this tool over siblings like get_filings or search_filings. It neither states when-not-to-use nor mentions alternatives.

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

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

No annotations exist, so the description must disclose behavioral traits. It mentions default date range of next 7 days, which is helpful. However, it does not describe output structure (e.g., format of returns, whether estimates are consensus or actual). Without annotations or output schema, more detail is needed 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?

Two precise sentences with a usage directive, no redundant words. Information is front-loaded, making it quick 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?

Covers the primary use cases adequately but omits details on output fields (e.g., what exact data is returned). Given no output schema, the description should be more complete for an agent to use 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?

Schema provides no descriptions for the three parameters (0% coverage). The description only vaguely references 'no dates specified' and 'specific company', but lacks details on parameter format (e.g., date format, ticker symbol conventions). Users are left to infer correct usage.

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 upcoming earnings announcement dates along with EPS and revenue estimates. This distinguishes it from sibling tools like 'get_filings' or 'get_guidance', which focus on different 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?

Explicitly describes when to use: for upcoming earnings, weekly reports, or a specific company's next earnings. Provides clear context for selection among 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?

No annotations are provided, so the description carries the full burden. It indicates a read operation ('Get') but does not disclose any behavioral traits such as rate limits, data freshness, or error conditions. 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?

Extremely concise with two sentences. The first sentence states the purpose and required parameters, the second provides usage context. 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?

Given the simple parameter set and absence of output schema, the description covers the core functionality and usage. However, it does not describe the output structure or potential errors, leaving some completeness gaps.

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

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

Schema description coverage is 0%, but the description adds meaning by specifying the parameters (ticker and accession number) and provides an example format for accession_number. This significantly aids understanding beyond the bare schema.

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

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

The description clearly states the tool gets the full detail of a single SEC filing by ticker and accession number. It distinguishes from siblings like get_filings and get_guidance by specifying it's for a single filing's detail.

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

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

Explicitly instructs to use this tool after get_filings or get_guidance has identified a specific filing, providing clear when-to-use guidance and context.

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

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

No annotations are provided, so the description must carry the full burden. It discloses that the tool returns dates and accession numbers, implying a read-only operation, but does not mention whether it is destructive or any side effects. It could be more explicit about its read-only nature and any limits or authorization needs.

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. The first sentence states the core purpose, and the second provides usage context and an alternative. 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 no output schema, the description usefully states that the tool returns dates and accession numbers. It also clarifies scope (guidance not included). However, it does not mention pagination, ordering, or potential limits, which could affect completeness for agents.

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 adds significant meaning. It explains that 'ticker' identifies the company and 'filing_type' filters by form type (e.g., 8-K, 10-Q). This goes beyond the schema's bare parameter names, though it could clarify the default behavior when filing_type is null.

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's purpose: retrieving SEC filings (8-K, 10-Q, etc.) by ticker. It clearly identifies the resource (SEC filings) and action (Get), and differentiates from sibling tool get_guidance by stating what it does not include.

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

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

Provides explicit guidance: 'Use this to see what filings are available for a company' and correctly directs to an alternative tool for guidance extraction. This helps the agent decide when to use this tool versus get_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 of behavioral disclosure. It reveals that results are keyword-matched sentences from filings and may include both forward guidance and reported results, which is critical for correct interpretation. It does not cover authentication or rate limits, but the key behavioral insight is present.

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, with the first sentence stating the core purpose and the second providing usage guidance and a behavioral note. Every sentence adds value, and the structure is front-loaded.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is largely complete. It covers purpose, when to use, and a behavioral caveat. It could be slightly more specific about the output format (e.g., list of sentences), but the note that results are 'keyword-matched sentences' provides adequate 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 single parameter 'ticker' is implicitly described as a ticker symbol in the description ('by ticker symbol'). Although the schema coverage is 0% per the signal, the description adds context for the parameter. For a simple string parameter, this is sufficient; no further details are 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 specifies that the tool retrieves forward-looking guidance language from SEC filings by ticker symbol. It uses a specific verb ('get') and resource ('guidance language extracted from SEC filings'), and distinguishes itself from sibling tools, none of which focus on guidance.

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 states when to use the tool: when asked about a company's future performance, revenue outlook, margin expectations, or other forward-looking statements. It also notes that results are keyword-matched and may include historical figures, advising careful reading. While it doesn't mention when not to use it or provide alternatives, the context is clear enough for an AI agent.

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 notes that sector names are case-insensitive, a useful behavioral detail. However, with no annotations, it does not disclose important traits such as pagination, data freshness, rate limits, or error handling for invalid sectors. Moderate 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 concise with two short paragraphs, front-loading the core purpose. Every sentence adds value: main purpose, case-insensitivity hint, list_sectors reference, and optional filter. 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 tool with 2 parameters and no output schema, the description is quite complete. It covers what the tool does, how to use parameters, and a prerequisite tool. Missing details about return format or result limits, but acceptable given 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 description adds significant meaning beyond the schema: it specifies sector examples, case-insensitivity, and lists possible event_type values. Since schema coverage is 0%, the description compensates well, though event_type options could be enumerated more precisely.

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 (SEC filings), and scope (all companies in a sector). It distinguishes from siblings like get_filings and search_filings by being sector-specific. Examples are provided.

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 if unsure of sector names, which is helpful. It also lists optional event_type filter. However, it does not explicitly mention when not to use this tool (e.g., for a specific company, use get_filings), which would improve 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 full burden. It discloses that signals are real-time, generated by Claude Haiku, and derived from EDGAR filings. This covers key behavioral aspects, though it could mention limitations like potential latency or confidence interpretation.

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?

Every sentence serves a purpose. The description is front-loaded with main function, then details, then usage guidance. Bullet points improve readability without waste.

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

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

Given no output schema, the description lists output components (ticker, classification, etc.). Two optional parameters are explained with defaults. Context is sufficient for 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?

Schema coverage is 0%, so description must compensate. It explains hours (default 720, set 24 for today) and mentions limit default 20 but does not detail the limit parameter's purpose. Value is added beyond schema but incomplete for limit.

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 real-time AI sentiment signals from SEC 8-K filings, including detailed components like classification and confidence. It distinguishes from siblings such as get_filings and get_company_info, making 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 explicitly mentions when to use: for bullish/bearish 8-K queries or screening sentiment shifts. It also provides practical tips like default window and setting hours=24 for today only. However, it does not explicitly mention when not to use or name alternative 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?

Describes return format (list with fiscal_quarter, fiscal_year, date, speakers) and discloses the limitation that it does not filter for a specific quarter server-side. Without annotations, this is reasonably transparent for a 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?

Four sentences, each adding value: purpose, return structure, usage nuance, and when-to-use guidance. No 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?

Adequately covers what the tool does and how to use its one parameter. Output structure is described, and no output schema exists. Guidance for quarter-specific queries is provided.

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 required parameter (ticker) is not described further, but context implies it's a stock ticker. Schema coverage is 0%, so description should add more; however, the parameter is simple and implicit.

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

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

Clearly states it retrieves earnings call transcripts by ticker, and distinguishes from SEC filings in sibling tools.

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

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

Explicitly tells when to use (earnings call questions) and contrasts with written SEC filings. Also advises looking for specific quarters in the returned list due to lack of server-side filtering.

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 carries burden. Discloses return value (11 GICS sectors covering S&P 500) and that it includes ticker counts. Does not mention any side effects or auth requirements, but for a read-only list with no parameters, 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?

Two concise sentences, front-loaded with purpose. Every sentence adds value: first defines action and output, second gives usage guidance and specificity.

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 parameters and no output schema, description fully covers purpose, usage, and output content. No 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?

No parameters, so baseline 4. Description adds meaning beyond schema by stating output includes ticker counts and scope (S&P 500, GICS sectors).

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 'List all available sectors with the number of tickers in each'. Specific verb (List) and resource (sectors), with additional detail on output. Distinguishes from siblings which focus on individual companies or filings.

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

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

Explicitly says 'Use this when asked about sectors, industries, or to find which sector a type of company falls under'. Provides context for when to use, though does not include when not to use.

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

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

No annotations provided, so description must cover behavior. It mentions search scope ('all SEC filings') and date format but omits read-only nature, pagination, or limits.

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 well-crafted sentences, front-loaded with purpose and parameters, followed by usage guidance—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 bare schema, no output schema, and no annotations, description fairly covers purpose, usage, and parameter format; could add result format details but not essential.

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%, description adds meaning to query (keyword), ticker (filter), and dates (YYYY-MM-DD format), but does not detail inclusivity/exclusivity of date range.

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?

Clear verb ('Search across all SEC filings'), resource identified, and distinguishes from siblings by specifying open-ended use vs. known ticker 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?

Explicitly states when to use (open-ended questions) and when not (looking up one known ticker), implying alternatives like get_filings.

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