Verified financial statements and derived metrics for a US-listed ticker, extracted from SEC EDGAR XBRL filings. Every value carries provenance (the exact filing, form, and tag it came from); missing facts are null, never 0. $0.25/call after free trial.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
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.
Add one secure layer between your agents and this server.
Each tool has a clear role: sec_extract retrieves raw data, validate_financials checks specific metrics, deal_score assesses a deal profile, and company_report produces a comprehensive report. Minor overlap exists between deal_score and validate_financials (both assess financial integrity), but descriptions distinguish them well enough.
All names are snake_case and readable, but they mix noun-first (company_report, deal_score), verb-first (validate_financials), and abbreviated noun-verb (sec_extract) patterns. This inconsistency is noticeable but not confusing.
Four tools is well-scoped for a financial analysis server: extraction, validation, deal scoring, and comprehensive reporting. Each tool earns its place without redundancy or bloat.
The tool set covers the full analysis pipeline: pull verified data (sec_extract), validate it (validate_financials), score specific deals (deal_score), and generate a comprehensive company report (company_report). No obvious dead ends or missing critical operations.