A rules-based 0-100 read of how well one US stock fits scheduled recurring buying (dollar-cost averaging), computed from its disclosed filings and daily bars. Structural description only: it is not a recommendation, not a price target, and not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | US exchange ticker symbol, e.g. AAPL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation already declares readOnlyHint=true, and the description builds on that by explaining the computation inputs (filings and daily bars) and framing the output as structural only, not advice. It does not cover update frequency or exact output format, but for a read-only tool this is adequate.
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 focused sentences with no filler. The core purpose is front-loaded, and the caveats are placed clearly at the end.
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 one-parameter, read-only tool with no output schema, the description covers the purpose, input, output scale, computation basis, and disclaimers. This is sufficient for an agent to select and invoke 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 schema provides full coverage for the single ticker parameter, so the description does not need to restate it. The description adds the US-stock context and DCA focus, but no additional parameter-format or syntax detail beyond 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 clearly states a specific verb ('read') and resource ('one US stock'), explaining that it produces a rules-based 0-100 score for dollar-cost averaging suitability. This distinguishes it from sibling tools such as buyback_leaders or risk_filings.
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 when to use it—for assessing how well a stock fits scheduled recurring buying—and adds exclusions by stating it is not a recommendation or investment advice. It does not explicitly name alternatives or say when not to use it, but the context is clear.
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.