Recent insider (Form 4) transactions for a company plus a trailing summary: open-market buys vs sales, distinct buyers, net dollar value, with 10b5-1 plan flags. Cluster buying by officers/directors is a studied positive signal. Not investment advice. Cost: $0.02.
| Name | Required | Description | Default |
|---|---|---|---|
| cik | Yes | ||
| months | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses cost ($0.02) and disclaims 'Not investment advice', but does not mention read-only nature, data freshness, rate limits, or any destructive potential. The trailing summary is described, adding some value.
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 with a short note, efficiently conveying the tool's purpose and output. It front-loads the core functionality. However, it could be slightly more structured by separating parameter guidance.
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 0% schema coverage and lack of parameter descriptions in the tool definition, the description is incomplete. It does not explain what cik or months represent, leaving the agent to guess. The presence of an output schema partially mitigates missing return value documentation.
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% (no parameter descriptions in schema). The description does not explain the two parameters: cik (required integer) and months (optional integer, default 12). Without context, the agent cannot infer what values are appropriate for these parameters.
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 recent insider Form 4 transactions and provides a trailing summary with metrics like open-market buys/sales, net dollar value, and 10b5-1 plan flags. It distinguishes from sibling tools by specifying 'insider (Form 4)' and 'open-market buys vs sales', which is not covered by tools like institutional_ownership.
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 for insider trading analysis and mentions cluster buying as a positive signal, but does not explicitly state when to use this tool versus alternatives (e.g., institutional_ownership). No guidance on when not to use or prerequisites.
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 distinct purpose: quality scoring, valuation, raw facts, filings, financials, peer comparison, industry aggregates, insider activity, institutional ownership, market insights, screening, short pressure, and standard scores. No overlaps in functionality.
All tools use consistent snake_case naming with descriptive prefixes (e.g., company_, buffett_, market_). The pattern is predictable and helps infer tool purpose.
14 tools cover a broad range of SEC fundamentals without being overwhelming. Each tool serves a clear role, and the count is appropriate for a comprehensive financial data API.
Covers core areas: filings, financials, insider trades, institutional ownership, screening, scores, and market insights. Minor gaps (e.g., no direct analyst estimates or corporate events) but overall very well-scoped for the domain.