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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable behavioral context beyond those: 'no look-ahead bias' for as_of, the exact provenance details (accession, filed date, URL), and the fact that ratios are 'derived from the same statements'. This gives agents a deeper understanding of the tool's behavior without contradicting the annotations.

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, well-structured paragraph that front-loads the core purpose before diving into parameter guidance. It covers all necessary details in about four sentences without redundancy. It is concise for a tool with six parameters, though slightly longer than minimal; still, 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?

Given the tool's moderate complexity (6 parameters, no output schema) and the annotations covering safety, the description is quite complete. It explains the return object type ('normalized data object') and lists the included statements (income statement, balance sheet, cash flow), which gives agents a sense of the output structure. It doesn't detail every possible return field, but with no output schema, this is acceptable. The absence of error conditions or rate limits is a minor gap, but not critical for a read-only 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 100%, so the baseline is 3. The description adds significant meaning beyond the schema: it clarifies that ticker and cik are alternatives ('OR'), explains as_of as 'no look-ahead bias' for backtests, and specifies what include_provenance attaches (SEC accession, filed date, EDGAR URL). These enrich the parameter semantics beyond the schema's brief descriptions.

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 ('Retrieve'), a clear resource ('normalized US-GAAP financial statements'), and a precise scope ('for one company and fiscal year'). This clearly distinguishes it from siblings like get_financials_batch (which implies batch/multiple) and get_revisions (which implies changes over time). The mention of SEC EDGAR XBRL adds a definitive source context, leaving no ambiguity about what this tool does.

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 contextual guidance: as_of is recommended for backtests (explicit use case) and include_provenance for citation. It also states 'Identify the company by ticker OR cik', clarifying the identifier options. However, it does not explicitly mention when to use alternatives like get_financials_batch or get_revisions, though the single-company/year scope implies those. The lack of explicit exclusions keeps it from 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?

Annotations already show read-only, open-world, and idempotent behaviortin less detail. The description adds significant behavioral context: per-company data-or-error responses with recovery hints, batch failure isolation, no-look-ahead as_of semantics, and single-request quota accounting. This exceeds what annotations provide.

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

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

The description is compact and well-structured: it leads with the primary purpose, then covers input constraints, parameter behavior, error handling, and quota implications in a logical order. Every sentence contributes actionable information 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 batch tool with no output schema, the description covers the key operational aspects: input format, limits, shared year constraint, per-symbol error handling, and quota implications. It also routes to get_financials for single-company needs. Nothing critical is missing for correct 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 coverage is 100%, so all parameters are documented in the schema. The description adds value by explaining aggregate semantics not visible in the schema: the combined 25-symbol limit across tickers and ciks, all symbols sharing one year, and that shared parameters behave identically to get_financials.

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 and resource: 'Retrieve normalized US-GAAP financials for MANY companies in one call.' It clearly distinguishes itself from the sibling get_financials by emphasizing batch retrieval and peer comparison/scans.

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 the tool: 'compare peers or scan a set for a single fiscal year,' and directly names the alternative get_financials while giving a concrete condition ('prefer it over many get_financials calls when you need several companies').

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?

Goes beyond readOnly/idempotent annotations by detailing the exact returned content: every reported value with filing date, form, SEC accession, EDGAR link, restated flag, and magnitude. It also explains the restatement concept, which helps the agent interpret results correctly.

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 front-loaded with the core behavior, then returned fields, then use cases and parameters. Every sentence carries unique information, including the sibling contrast.

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?

Even without an output schema, the description enumerates the returned fields and fully covers parameter behavior. It addresses the closest alternative tool and typical use cases, so an agent has everything needed to select and invoke it 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 description coverage is 100%, so the schema already documents ticker/cik and metrics. The description reinforces the ticker-OR-cik requirement and provides a metrics example, but adds no substantive semantics 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?

States a specific verb-resource pair: show how reported figures for a fiscal year changed across filings. It distinguishes itself from the closest sibling, get_financials, by contrasting the whole series with the latest figure.

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 lists when to use it: answering whether a figure was restated, showing a figure's history, and explaining backtest mismatches. It also names the alternative (get_financials and its as_of parameter) and explains why this tool is chosen instead.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety and idempotency. The description adds that it does not consume quota, which is a valuable behavioral detail beyond the annotations. However, it doesn't describe the exact format of the response (e.g., whether it's JSON, field names), relying on the output schema, which is not provided.

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 that front-load the key information (what it reports and that it's free), then adds usage guidance. No wasted words, 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 zero-parameter tool with strong annotations, the description is nearly complete. It explains the return content and the non-consuming nature. The only minor gap is the lack of an explicit mention of the response format, but since there's no output schema and the tool is simple, this is a small omission.

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 description correctly focuses on what the tool returns and its side effects. With no parameters to describe, the description fully covers the input semantics, making it 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 reports the calling API key's monthly quota details (tier, limit, usage, reset), which is specific and distinct from siblings like get_financials or list_filings. It uses a clear verb ('report') and resource ('calling API key's current monthly quota'), making it 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 says it is free to call and does not consume quota, and it recommends checking it before large batches to avoid rate limits. This provides clear context for when to use it, though it doesn't mention alternatives—but given this is a unique monitoring tool, that's acceptable.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable behavioral context: newest-first ordering, prefix-matching for form types (e.g., includes 10-K/A), and output shape (a lean company header plus filing rows).

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, action-first, and includes all key details (identification, filters, limit, order, use case) without 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 4-parameter filtered list tool with no output schema, the description explains input, filtering, ordering, and intended use before get_financials. It stops short of listing specific filing row fields, but the mention of discovering fiscal years/filings gives sufficient context for an agent to use it 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 description coverage is 100%, so the baseline is 3. The description repeats the ticker-or-cik relationship and prefix-matching behavior already present in the schema, adding minimal new semantics 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?

States a specific verb ('List'), resource (SEC filings from the EDGAR submissions index), and order (newest first). It also names the sibling get_financials as the follow-up, clearly distinguishing this tool's role as a discovery step.

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 to discover which fiscal years or filings are available before calling get_financials,' providing a concrete use case and naming the alternative for actual financial data. The ticker-or-CIK identification and filter options further clarify when to use 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful behavioral context beyond the annotations: it searches SEC's master list, ranks exact ticker matches above name-contains matches, and caps results via limit. This is meaningful but not exhaustive (e.g., no mention of response structure or error cases).

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 compact sentences, each earning its place: purpose, usage mandate, source/examples, and ranking/limit behavior. The structure front-loads the key instruction and uses line breaks to separate distinct ideas 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 2-parameter, read-only tool with no output schema, the description covers what, when, how, and expected output examples. It even explains ranking behavior and limit constraints, so an agent has everything needed to invoke it correctly and interpret results at a practical level.

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 functional meaning beyond the schema: query accepts either a name or ticker with real examples ('berkshire' returns BRK-B), and limit's purpose is reinforced ('caps the number of matches'). This elevates the parameter understanding beyond the basic schema definitions.

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

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

States a specific verb and resource: finds a company's ticker and CIK by ticker or company name. It clearly positions itself as the prerequisite lookup tool that other sibling tools implicitly depend on, distinguishing it from get_financials, list_filings, etc.

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 'Use this FIRST whenever you have a company name but not its ticker or CIK' and notes 'every other tool needs one of those.' This gives an unambiguous when-to-use rule and implies when-not-to-use (when identifiers are already known), with concrete examples.

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