Recent SEC filings whose text MENTIONS a specific risk phrase, per EDGAR's full-text index. A mention is not a finding — the phrase also matches negations like "no material weakness". Values: going-concern, material-weakness, cyber-incident.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | Yes | which saved risk query to run |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, and the description adds meaningful behavioral context: a mention is not a finding, and the phrase matcher also catches negations like 'no material weakness.' This warns the agent about a real false-positive class.
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 tight and front-loaded: function, source, caveat, and supported values in three short sentences. Every sentence adds value with no filler or redundancy against 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?
For a simple one-parameter, read-only tool with full schema coverage and no output schema, the description is complete. It covers purpose, source, allowed values, and a key limitation. No critical missing context is apparent.
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 covers the single 'kind' parameter fully with an enum and description. The tool description adds semantics by explaining that these values correspond to risk phrases and that results are based on text mentions, including negation cases, which helps the agent choose correctly.
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 SEC filings whose text mentions a specific risk phrase via EDGAR's full-text index. This is distinct from sibling tools like buyback_leaders and congress_trades, so purpose and resource are unambiguous.
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 for when to use the tool: screening SEC filings for risk-phrase mentions, with the supported values enumerated. It does not explicitly discuss when not to use it or name alternatives, but the use case is obvious from the description and sibling context.
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 source or analytical angle: buybacks, congress trades, DCA fit, insider buying, 13F filings, price levels, risk phrases, and options volume. There is no overlap in purpose or output, so an agent can unambiguously select the right tool.
All tool names follow a consistent lowercase_with_underscore noun-phrase pattern (e.g., buyback_leaders, risk_filings). The naming is uniform and descriptive, making it easy to predict what each tool does based on the name alone.
The server has 8 tools, squarely in the well-scoped range. Each tool represents a meaningful, non-redundant screen or data source, and none feels like filler or duplicative functionality.
For a market-aware server, the coverage is strong: it spans corporate actions (buybacks), insider activity, institutional holdings, technical levels, risk signals, and options flow. Minor gaps exist—no general price/quote tool or earnings-focused screen—but the set is coherent and covers diverse data sources without major dead ends.