Mined alpha expressions from the research pipeline, filtered to a minimum information ratio (min_ir, default 0.5) and capped at limit (default 25). Call this when the user asks what alpha signals or factor expressions the research engine has actually discovered.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| min_ir | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It discloses key behavioral traits: filtering by min_ir, capping by limit, and default values. However, it does not explain the output format, ordering, pagination, or any potential side effects, leaving notable gaps in behavioral transparency for a tool with no annotation support.
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 consists of two sentences, both highly informative. It front-loads the core purpose and then specifies usage context without any wasted words or repetition of schema details.
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 (2 optional parameters, no output schema), and the description covers purpose, parameters, and usage context. It lacks explicit details about the return value structure, which could be helpful given no output schema exists, but the description is otherwise complete for its complexity.
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?
With schema description coverage at 0%, the description compensates by clarifying the meaning of both parameters: 'minimum information ratio (min_ir, default 0.5)' and 'capped at `limit` (default 25)'. This adds semantic value beyond the schema's type and bounds, though it does not fully describe all behavioral nuances of these parameters.
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's function: 'Mined alpha expressions from the research pipeline' with specific filtering and capping parameters. It distinguishes itself from sibling tools by explicitly mentioning alpha signals/factor expressions discovered by the research engine, a unique scope among the listed siblings.
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 a clear when-to-use directive: 'Call this when the user asks what alpha signals or factor expressions the research engine has actually discovered.' It does not name specific alternatives or provide when-not-to-use guidance, so it falls short of a 5, but the usage context is unambiguous.
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.
