Signal Ledger Financial Intelligence

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

Annotations already establish readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond this: the tool is paid ($0.25 USDC), it automatically fetches SEC filings for symbol input, and it returns evidence-linked, machine-readable risk findings. There is no contradiction with 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 front-loaded with the most critical information (paid cost, purpose) and then efficiently covers input modes and output characteristics. The bilingual content doubles the length but is a deliberate localization choice rather than filler; each sentence in both languages carries meaning without redundancy within a language.

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 rich schema with fully documented parameters and an explicit output schema, the description needs only to supply operational context. It covers cost, input options, automatic SEC retrieval, and the nature of the output (evidence-linked, machine-readable). No critical missing information prevents an agent from invoking 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 input schema provides descriptions for all 7 parameters (100% coverage), so the baseline is 3. The description reaffirms the two key input options (report_text and symbol) but does not add significant meaning beyond the schema's own guidance, which already states these modes. Other parameters (period, report_type, source_context, etc.) are fully documented in 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 opens with a specific verb ('analyze') and a clear resource ('financial-statement risk'), then enumerates concrete risk dimensions (earnings quality, cash-flow anomalies, accounting red flags, debt pressure, governance, data quality). This leaves no ambiguity about what the tool does, and it is clearly distinct from the sibling get_author_track_record, which deals with author reputations rather than financial reports.

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 the two valid input modes: supply report text, or provide a US stock symbol to trigger automatic SEC filing retrieval. It gives clear context for how to use the tool, though it does not explicitly discuss 'when not to use' or compare alternatives. Since the only sibling is unrelated, this is not a significant gap.

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 mark it read-only and idempotent; the description adds useful behavioral details beyond those, notably the $0.02 USDC cost and that it returns resolved prediction history with sample size, hit rate, confidence interval, and freshness. No contradictions.

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, front-loads the key purpose and cost, and avoids fluff. The English and Chinese versions are consistent and appear in the same string without clutter.

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?

There is an output schema, and the three parameters are fully documented in the schema. The description adds useful context about cost and typical use cases. It does not need to explain return values, so nothing important 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 coverage is 100%, and the description does not add parameter-level meaning beyond listing result metrics. The schema already documents handle, window_days, and type, 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 uses a specific verb (

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: it is for creator credibility, influencer reputation, and due-diligence workflows. It does not explicitly contrast with the sibling tool analyze_financial_report_risk or state exclusions, so it stops short of a perfect score.

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