sec_eventsRecent SEC EDGAR corporate events as flagged signals: 8-K items (bankruptcy, auditor change, cyber incident...), Form 4 insider buys/sells with USD size and buy clusters, Form D raises. $0.005/call. Not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| cik | No | ||
| flags | No | ||
| forms | No | ||
| limit | No | ||
| since | No | ||
| ticker | No | ||
| min_score | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context beyond that: it is a flagged-signals feed with specific event types, has a per-call cost of $0.005, and carries a not-investment-advice disclaimer. This is meaningful extra context for an agent deciding whether to call it.
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 compact sentences with the core domain content front-loaded, followed by cost and disclaimer. Every sentence contributes: scope, pricing, and regulatory caveat. There is no filler or redundant restatement of the schema.
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 though an output schema exists, the tool has seven optional parameters with zero schema descriptions. The description is not sufficient for correct invocation beyond a bare default call; it never explains filtering semantics, score thresholds, date formats, or how flags are structured.
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 0%, so the description should compensate, but it only indirectly maps Form 4/8-K/Form D to the `forms` parameter. The other six parameters (cik, ticker, flags, limit, since, min_score) are left entirely unexplained, forcing the agent to guess their formats and meaning.
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 names a specific resource (SEC EDGAR corporate events) and enumerates concrete content categories (8-K items, Form 4 insider trades, Form D raises). This clearly distinguishes it from siblings like sec_company or funding_signals and leaves no doubt about what the tool returns.
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?
Usage is implied rather than stated: an agent can infer this tool is for recent SEC event signals, but there is no explicit guidance about when to prefer it over alternatives or when not to use it. Sibling names like funding_signals suggest overlap, but the description offers no routing help.
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 targets a distinct data domain: billing/product info, federal funding, federal regulations, SEC filings/events, and research topic analytics. The three technology_* tools are related but differentiated by scope (ranked list, snapshot, deep analysis), so an agent should be able to select correctly with descriptions. Minor potential confusion exists between funding_signals and sec_events since both emit 'signals', but they cover clearly different sources.
All names are snake_case and map to clear domains, but the pattern is not uniform: list_products uses verb_noun, while funding_signals, reg_rules, sec_company, and technology_lookup use noun-style names. The sec_* and technology_* prefixes help navigation, but there is no consistent verb convention across the set.
Nine tools is a well-scoped size for a data-as-a-service server. It includes meta tools (credit, product list) and seven data products without obvious redundancy. Each tool appears to earn its place.
As a read-only data API, full CRUD is not expected, and the server covers billing, product selection, federal money, regulations, SEC events/company snapshots, and emerging tech analytics. The main gap is a lack of a general search/discovery tool, but the specialized endpoints cover their stated purposes well.