Finnhub financial data: earnings calendar, general/crypto news, company profiles. Requires FINNHUB_API_KEY.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-50, default 20) | |
| action | Yes | ||
| symbol | No | Stock symbol for company/earnings filter | |
| to_date | No | End date YYYY-MM-DD (earnings) | |
| from_date | No | Start date YYYY-MM-DD (earnings) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only discloses the API key requirement but does not state whether the tool is read-only, rate limits, or side effects. The lack of behavioral context beyond the prerequisite is a significant gap.
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, short and front-loaded with the tool's purpose. It is concise, though it could include more behavioral or parameter insight without being verbose.
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 5 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, how to use each action with symbol and dates, pagination, or default limit. The schema descriptions partially cover parameter details, but overall context for usage is lacking.
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 80%, so baseline is 3. The description adds value by mapping the listed data types to the action parameter's enum values (earnings, news, crypto_news, company) and implying usage of 'news' for general and crypto. This provides context beyond the schema's enum alone.
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 Finnhub financial data: earnings calendar, general/crypto news, and company profiles. It identifies the verb (provides) and the resource (Finnhub data). However, it does not distinguish this tool from the sibling 'news_feed', and the tool name 'tradfi_news' could mislead about the inclusion of crypto news.
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 only mentions a prerequisite (requires API key) but provides no guidance on when to use this tool versus sibling tools like 'news_feed' or 'backtest'. There is no mention of what actions are appropriate for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Multiple tools overlap in providing crypto price data (crypto_price, crypto_market, crypto_derivatives, global_assets) and news (news_feed, tradfi_news). While descriptions clarify some differences, agents may struggle to choose between similar tools.
Tool names mix styles: some are simple nouns (backtest, cn_market), others compound nouns with underscores (crypto_derivatives, derivatives_sentiment). No consistent verb_noun pattern, making it harder to infer purpose from name alone.
With 19 tools covering a broad domain (crypto, DeFi, macro), the count feels slightly high but justifiable. However, several tools could be merged to reduce overlap and improve navigability.
The tool set covers a wide range of market intelligence needs: price data, technical analysis, sentiment, macro indicators, news, and DeFi. Minor gaps like on-chain analytics or direct trading are acceptable given the intelligence focus.