Recent unusual options-flow alerts across the whole market from the options-flow feed, filtered to trades above min_premium (default $50k). Call this when the user asks 'what is the smart money buying today?' or wants market-wide unusual options activity; use tengu_v3_intel_options_flow_ticker for a single name.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| freshness | No | off | |
| min_premium | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden of disclosing behavior. It reveals that the tool returns recent alerts, filters by a minimum premium (defaulting to $50k), and sources from the options-flow feed. While it doesn't discuss response format or rate limits, the core behavior is transparent and consistent with schema defaults. The description does not contradict any annotations (none provided).
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, with the first stating the core function and the second providing usage guidance and an alternative. It is front-loaded with the essential information and contains no irrelevant words. This is an exemplar of concise, structured tool documentation.
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 description covers the main use case and mentions the key filter, but it lacks explanations for the limit and freshness parameters, which are not self-evident from the schema. Additionally, since there is no output schema, the description does not hint at the structure of the returned alerts. This leaves some uncertainty for an agent preparing to invoke the tool, but the overall purpose is clear.
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?
The schema has a 0% description coverage, so the description is the only source of parameter meaning. It explains min_premium (the dollar threshold for trades), but does not clarify the 'limit' or 'freshness' parameters. Since the schema itself lacks descriptions, this leaves two out of three parameters under-documented, which 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 that the tool returns recent unusual options-flow alerts across the whole market, filtered by a minimum premium. It uses specific language ('across the whole market', 'from the options-flow feed') and distinguishes itself from the ticker-specific sibling tool. This gives an agent a precise understanding of the tool's function.
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 provides explicit guidance on when to call this tool: when the user asks about 'smart money buying today' or wants market-wide unusual options activity. It also names the alternative tool for single-name queries, making the usage boundary clear. This is exactly the kind of when-to-use and when-not-to-use guidance the rubric asks for.
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.
