agentic-ai

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

Discloses SEC-verified basis, per-field citations, caller-asserted context, cost, and runtime. With no annotations, this is substantial. Lacks error handling details, but acceptable for a report 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?

Front-loaded with core purpose, then lenses, optional params, and cost/time. No wasted words; clearly organized.

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?

High complexity tool with no output schema or annotations; description covers inputs, trust levels, cost, and time. Missing explicit return format but functionally 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?

Explanation of market and context parameters with example and trust-level distinction adds meaning beyond bare schema. Ticker is self-evident from the name and description.

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?

Specific verb 'analysis' with resource 'US-listed company' and distinctive eight-lens structure. Clearly differentiates from sibling extraction/validation tools, but doesn't 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?

Clear context for when to use (comprehensive institutional analysis) and what optional inputs enable, plus cost/time. No explicit exclusions or alternatives, but the scope is well-defined.

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 cost per call and the structure of outputs (risk register, investment thesis, rNPV band), plus a conditional behavior for biotech. However, it does not explicitly state whether the tool has side effects, requires authentication, or if it is read-only, leaving some 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, compact sentence that front-loads the purpose and lists the analysis steps and outputs in a clear, ordered fashion. It also includes the cost, which is useful, without any unnecessary 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?

The description provides a solid overview of the analysis chain and expected outputs, and it mentions pricing. However, the tool is complex and the `deal` input object is entirely underspecified. Without an output schema or annotations, the description only partially guides an agent on how to invoke the tool and interpret results.

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 one unannotated object parameter with 0% description coverage. The description does not explain what fields the `deal` object should contain beyond hinting at `indications` for biotech deals. This is insufficient for an agent to construct a valid `deal` object.

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 performs a 'full underwriting chain' on a deal profile, listing specific analyses (financial integrity, trend, industry position, stress tests, conviction score) and outputs (risk register, thesis, rNPV). This distinguishes it from sibling tools like company_report and validate_financials, which focus on reports or validation.

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 use for comprehensive deal underwriting and mentions a conditional for biotech deals with `indications`, but it does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria. No alternative tools are named.

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 behavioral disclosure burden. It adds valuable traits: provenance for every value, missing facts as null (never 0), and cost ($0.25/call). It does not mention error handling or limits, but for a read-only extraction tool this is solid 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?

Three sentences, each earning its place: the first defines the resource, the second details data quality behavior, and the third notes pricing. 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 tool with one parameter and no output schema, the description is reasonably complete. It explains the source, the nature of the data, null handling, and cost. It could specify what financial statements include, but overall it provides sufficient context for an agent to decide and invoke.

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 only defines 'ticker' as a string with no description. The description adds the constraint 'US-listed ticker', providing some semantics beyond the schema. However, it does not elaborate on expected format or example, so it barely compensates for the 0% 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 identifies the tool's function: it provides verified financial statements and derived metrics from SEC EDGAR XBRL filings. This specific resource (financial data from SEC filings) distinguishes it from siblings like company_report or deal_score.

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 (US-listed ticker, verified financial data) implying when to use the tool. However, it does not explicitly mention alternatives or scenarios when not to use it, so there is no direct comparison 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses that the tool returns an integrity score and severity-ranked flags, and mentions the cost model. It does not explicitly state whether the operation is read-only, but 'validation pass' strongly implies non-destructive behavior. However, it omits details on error handling, required financial data structure, or rate limits beyond pricing, 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?

The description is compact, using a colon-separated list to enumerate validation checks in the first sentence and a second sentence for output and pricing. It is efficiently structured with no wasted words, though the dense list could be slightly harder to parse quickly. The pricing note is extra but useful.

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 (nested object parameter, no output schema, no annotations), the description is incomplete. It explains the output (integrity score + flags) and lists the checks, but it does not describe the input structure, nor does it provide guidance on when to use this tool versus siblings like sec_extract or deal_score. This leaves significant gaps for an agent to correctly 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 schema has a single 'financials' object parameter with no description and 0% schema description coverage. The tool description does not explain the structure or required fields within the financials object, leaving the agent without guidance on how to construct valid input. This is a critical gap.

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 an 'institutional validation pass' over company financials, specifying concrete validation checks like tax-rate sanity, EBITDA/EBIT bridge, and growth continuity. This specific verb+resource structure distinguishes it from sibling tools like company_report (report generation), deal_score (scoring deals), and sec_extract (extracting SEC 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 the tool is used for validating financial integrity, but it does not explicitly state when to use it over the sibling tools or provide exclusions. It lacks direct comparative guidance, so the usage context 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.