Today's top N picks (long or short side). CALL THIS when the user asks 'what should I buy', 'give me trade ideas', 'top picks today', 'daily briefing', or similar list-of-ideas queries. Each pick comes with the same full quant analysis as score_ticker (score, interval, voter breakdown, narrative).
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | ||
| side | No | long |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that each pick includes the same full quant analysis as score_ticker (score, interval, voter breakdown, narrative), which is useful behavioral context. However, it doesn't mention how picks are ranked, ordering, or any failure/edge-case behavior, leaving it at a minimally viable level.
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 three sentences with no fluff. It front-loads the purpose, then usage triggers, then output details. Every sentence adds value, and the structure is easy to parse.
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?
Given the low complexity (2 optional params, no output schema), the description covers the essential aspects: what it returns, when to use it, and what each pick contains. It doesn't describe edge cases or ranking criteria, but those are not critical for making a selection decision here.
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 explain the parameters. It does: 'top N picks' clarifies the n parameter, and 'long or short side' explains the side parameter. The schema already provides defaults and constraints, but the description adds semantic meaning beyond those.
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 returns today's top N picks with a long or short side, and explicitly provides example user queries that should trigger it. It distinguishes itself from single-ticker analysis by referencing score_ticker, making it clear this is a list-of-ideas tool.
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 explicit when-to-use triggers ('CALL THIS when the user asks...') with concrete examples. It doesn't explicitly state when not to use it or name direct alternatives like tengu_ml_top_picks, so it lacks exclusions but provides clear context.
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.
