Structured market events — earnings, M&A, FDA decisions, guidance changes, price-target moves — filterable by ticker, event_type, and date_range (default today). Call this when the user asks 'what events happened' or 'any catalysts for X' instead of scanning raw headlines.
| Name | Required | Description | Default |
|---|---|---|---|
| items | No | ||
| ticker | No | ||
| date_range | No | today | |
| event_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the output is 'structured' (not raw), that it is filterable, and that date_range defaults to today. It does not describe response format or pagination behavior, but the structured-concept and default behavior add meaningful context. A small gap, but not a serious omission for a read-only retrieval tool.
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 sentences, front-loaded with the core purpose, followed by parameter details and usage guidance. Every sentence adds value with no repetition of schema metadata. Highly efficient.
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 4-parameter retrieval tool with no output schema and no annotations, the description provides the essential context: what it returns, common event types, key filter parameters, default date range, and explicit when-to-use guidance. It lacks mention of the items parameter and specific date_range formats, but these are secondary given the tool's simplicity. Overall, sufficient for correct selection and basic invocation.
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 compensate. It mentions ticker, event_type, and date_range, and provides examples of event_type values (earnings, M&A, FDA decisions). However, it does not mention the 'items' parameter at all, and date_range format (e.g., 'today' vs specific ranges) is only partially described. The examples help, but the missing items parameter and vague date_range format represent a clear 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 'structured market events' with specific categories (earnings, M&A, FDA decisions, guidance changes, price-target moves). It is distinct from sibling tools like raw headline news tools, and the name 'curated_events' reinforces the specific resource.
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?
Explicitly instructs when to use: 'Call this when the user asks "what events happened" or "any catalysts for X"' and contrasts with 'scanning raw headlines'. This provides direct usage context and implies not to use it for headline scanning, which differentiates from siblings.
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.
With 336 tools, there is substantial overlap. Over a dozen health/status tools share nearly identical 'is the system healthy?' descriptions (e.g., tengu_status, tengu_ready, tengu_ml_health, tengu_v3_system_health, tengu_v3_stream_status), and multiple single-ticker analysis (tengu_ml_predict, tengu_copilot_score_ticker, tengu_v3_intel_ml_prediction) and top-picks (tengu_copilot_top_picks, tengu_ml_top_picks, tengu_v3_trade_setups) tools have poorly defined boundaries. Agents would frequently misselect.
The server mixes no-version (tengu_crypto), v2 (tengu_v2_drift), v3 (tengu_v3_intel_*), and copilot (tengu_copilot_*) families, and within families there is inconsistent verb/noun ordering (tengu_v3_research_fetch_url vs tengu_v3_news_summary). While subfamilies like tengu_v3_private_markets_* are internally consistent, the overall naming pattern is chaotic and unpredictable.
336 tools is far beyond any reasonable tool set size, even for an all-in-one financial data platform. This extreme count creates choice paralysis, high latency in tool selection, and makes the server effectively unusable for autonomous agents. The calibration guideline marks 50+ as extreme; this is nearly 7x that threshold.
The platform covers a vast domain: equity and crypto prices, fundamentals, insider trading, options, news (including crypto and FX), private markets, streaming data, risk metrics, and execution planning. There are minor gaps (no direct multi-ticker comparison tool, no order placement), but the surface is remarkably comprehensive for an analysis-focused server.
