fx_rateExperimental (outside the core SEC path; not covered by the published accuracy eval, may change or be withdrawn): ECB reference rates from one base currency to up to ten targets, ISO 4217. Daily fix, not a tradable quote. Costs $0.01 per call.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Comma list of targets. Default EUR. | |
| from | No | Base currency. Default USD. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description supplements this by disclosing that the rate is a daily fix (not real-time), that it is experimental and may change/withdraw, and that each call costs $0.01. These add practical behavioral context beyond the annotations, such as cost and stability, which are not captured structurally.
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 sentence but contains several pieces of information packed in a structured way: an initial caveat, the core purpose, a note on frequency, and cost. It is not wordy, but the caveat at the beginning is slightly long and could be moved to the end for better front-loading of the purpose. Still, it is efficient and every clause adds value.
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?
The tool is simple (two optional parameters, no required fields, no nested objects) and the description covers purpose, cost, and the daily-fix nature. However, with no output schema, the description should indicate what the return value looks like. It says 'rates from one base currency to up to ten targets' but does not specify the structure (e.g., a map, list, or single value). This omission means an agent may not know how to parse the result, making it incomplete on output semantics.
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 100% and the schema describes each parameter, but the description adds meaningful semantics: it specifies 'up to ten targets' and mentions ISO 4217, which clarifies the allowed values and format beyond the schema's generic 'comma list'. It also implies that 'from' is a single base currency, which is implicit but reinforced.
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 identifies the resource (ECB reference rates), the action (provide rates), and the scope (one base to up to ten targets, ISO 4217). It also distinguishes from siblings by noting it is not a tradable quote, which differentiates it from potential market data tools. The verb 'provides' is implicit but clear from context.
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 context: it is a daily fix, not a tradable quote, and costs money, which hints at suitability for reference use rather than live trading. However, it does not explicitly state when to use this tool versus alternatives, nor does it name any sibling tools or exclude use cases. The caveat about being experimental and not covered by accuracy eval is more about trust than usage.
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 written descriptions, but several pairs overlap semantically: token_price/token_report, latest_filings/recent_filings, and institutional_holdings/who_holds/activist_stakes all require careful reading to pick the right one. The overlap is not severe enough for a 2, but it is still more than the occasional confusion.
Names are consistently snake_case and mostly descriptive, but the set does not follow one naming convention. It mixes noun phrases (crop_data, token_report, insider_trades) with verb phrases (lookup_company, compare_filings, verify_financial_claim), and pairs like latest_filings/recent_filings create avoidable confusion.
31 tools is too many for what is nominally an SEC Filings server, and the scope sprawls into unrelated domains such as EVM gas, tokens, prediction markets, crop data, and x402 auditing. The count does not feel earned by a coherent workflow; it feels like an uncurated bundle of endpoints.
The SEC filing workflow is reasonably covered: company lookup, search, latest/recent filings, section extraction, 8-K events, comparisons, ownership filings, and XBRL financial checks are all present. However, there is no full-filing retrieval endpoint, no deep company financial history beyond a single concept, and many non-SEC subdomains are only one-off data points, so the overall surface is still incomplete.