Futures term structure for one CME root (27 roots incl. ES, NQ, CL, NG, GC, ZN) from FIRM's daily chain snapshots — per contract month: last/settlement, bid/ask, session OHLC, volume, open interest, days-to-expiry. Call it for curve shape (contango/backwardation), roll, or OI distribution; omit date for latest, snapshots begin 2026-05-18.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| root | Yes | Path parameter 'root' (required). | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the data source ('FIRM's daily chain snapshots'), history start date (2026-05-18), and field set, which is useful. However, it doesn't mention rate limits, error behavior, or any side effects—though this is a read-only retrieval tool, the lack of explicit read-only confirmation and other operational constraints keeps this at a middle score.
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 dense but efficient sentences. It front-loads the core concept ('Futures term structure') and packs in all essential details without redundancy. Every clause adds information, and there is no filler.
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 has no output schema, so the description must explain what is returned. It lists the per-contract fields comprehensively and covers a few key context points (source, start date, omit date for latest). However, it omits the effect of the 'limit' parameter, date format, and any ordering information, making it slightly incomplete for an integration agent.
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 only 33% and the schema's root description is tautological ('Path parameter root'). The description compensates by clarifying 'root' as a CME product code with examples (ES, NQ, CL, NG, GC, ZN) and implicitly explains 'date' via 'omit date for latest'. The 'limit' parameter is not mentioned at all, leaving its effect ambiguous. Some value added, but not enough to fully compensate for the schema 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's purpose: 'Futures term structure for one CME root' and enumerates the exact data fields returned per contract month. This distinguishes it from sibling tape tools (e.g., tape_futures, tape_options) which focus on different instruments or data types.
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?
It explicitly provides usage cases: 'Call it for curve shape (contango/backwardation), roll, or OI distribution' and gives a concrete operational tip: 'omit date for latest'. While it doesn't name alternative tools, the guidance is clear enough for an agent to decide when to invoke this tool.
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.
