★ SEC fundamentals — is the company actually growing & profitable?
Latest annual revenue + YoY growth, net income, net/gross margin from SEC XBRL (keyless, point-in-time by filing date). Use it to check whether an analyst's 'accelerating growth' narrative matches the reported numbers. FREE. Not advice.
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | ||
| ticker | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the data source (SEC XBRL), nature (keyless, point-in-time by filing date), and says 'FREE' and 'Not advice'. It does not discuss rate limits or authentication, but for a read-only query 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 a single focused paragraph with a clear lead question. It includes some fluff (star, 'FREE', 'Not advice') but remains concise. The core information is front-loaded.
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?
Output schema exists, so return value details are not required. The description covers the key metrics and source but lacks parameter semantics (as_of). Given the tool's simplicity and the 0% schema coverage, the description is incomplete regarding the optional parameter.
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 must explain parameters. It implicitly mentions 'ticker' via 'company', but does not explain the 'as_of' parameter at all. The agent cannot determine what 'as_of' does from the description alone. This is a significant gap.
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 provides SEC fundamentals (revenue, YoY growth, net income, margins) from SEC XBRL, with a specific use case of verifying analyst narratives. It distinguishes itself from sibling tools (e.g., quote, short_volume) by focusing on financial data and not price or sentiment.
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 a clear use case: to check if a company is growing and profitable and to validate analyst growth claims. It does not explicitly state when not to use or list alternatives, but the context of sibling tools makes the purpose distinct. This is good but could be more exclusionary.
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.
Most tools have clearly distinct purposes (e.g., analyst_views fetches views, analyst_debate compares them, analyst_track_record scores accuracy). Some overlap exists between sentiment tools (stocktwits_symbol, ticker_social_sentiment) but descriptions clarify boundaries. Overall, an agent can differentiate them.
Naming is mostly lowercase with underscores, but conventions vary: some use prefixes (analyst_, direction_review_), some are single words (quote, leaderboard), and others are verb_noun (score_ticker, screen_stocks). This inconsistency makes patterns less predictable, though prefixes help group related tools.
With 24 tools, the server is slightly above the ideal range of 3-15 for coherence. While each tool seems justified for the financial analysis domain, the volume could be overwhelming. Some tools (e.g., tweet_store_stats, direction_review_batch) are operator-only, reducing the surface for typical agents.
The tool set covers core workflows: fetching analyst views, tracking accuracy, SEC fundamentals, insider activity, material events, live quotes, social sentiment, and screening. Gaps like earnings calendar or portfolio management are minor given the focus on analyst-driven analysis. The operator tools for direction review add internal completeness.