catalysts_economic_calendarScheduled macroeconomic data releases (CPI, FOMC, jobs, GDP, etc.) from the last ~2 days through the next 7 days. Optionally set 'min_importance' (1-3, default 2) to filter out low-impact releases.
| Name | Required | Description | Default |
|---|---|---|---|
| min_importance | No | Minimum importance level to include: 1 (all), 2 (medium+), 3 (high only). Defaults to 2. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, so thedescription does not need to establish the read-only nature. The description adds useful behavioral context: the time range, the type of content, and how min_importance changes the result set. It does not disclose return structure or pagination, but for this simple read-only tool the added behavior is sufficient.
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?
Two short sentences cover the core purpose, time window, and optional filter behavior with no wasted words. The most important information appears first, and every clause contributes to an agent's ability to invoke the tool correctly.
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 read-only tool with one optional parameter and no output schema, the description provides the essential content, schedule scope, and filtering option. It could be more complete by describing the returned fields or contrasting it with catalysts_calendar, but those are minor gaps relative o the tool's simplicity.
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?
There is only one parameter and the schema already documents it fully, including the 1-3 scale and default of 2. The description essentially restates the schema's meaning rather than adding new semantic or edge-case detail, so the baseline score of 3 is appropriate.
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 as scheduled macroeconomic data releases with concrete examples (CPI, FOMC, jobs, GDP) and an explicit time window. It does not use a direct verb like 'list' or 'get,' and it does not explicitly differentiate itself from the sibling catalysts_calendar, so it falls just short of a 5.
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 the tool is appropriate: macroeconomic releases within roughly the last 2 days to next 7 days. It also explains the optional min_importance filter. However, it does not state when to prefer an alternative tool like catalysts_calendar, so no exclusions are provided.
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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Tools are grouped by clear prefixes and mostly target distinct resources; market_quotes vs research_token_view, market_top_movers vs market_trending, and funding_current vs market_quotes have some field or purpose overlap, but descriptions draw enough scope boundaries for an agent to choose correctly in most cases. No two tools are truly interchangeable.
All 30 tool names use lowercase snake_case with a consistent domain-prefix convention such as market_, flow_, research_, sentiment_, and catalysts_, making the surface predictable. Even helpers like utc_time and risk_position_size fit the same noun-oriented pattern without style mixing.
At 30 tools this set crosses the 'too many' threshold, and several tools reproduce data already available through broader ones such as research_token_view and market_quotes. The breadth is defensible for a crypto-research platform, but the surface feels heavy and could be consolidated.
The tool set covers the read-only crypto research workflow thoroughly: market data, candles, derivatives, funding, OI, order book, whale flows, ETF flows, on-chain metrics, news, sentiment, prediction odds, technicals, regime/positioning scans, and position sizing. It also includes health and UTC helpers that close practical workflow gaps, with no obvious dead ends.