Last N quarters of earnings for a ticker — report date, EPS estimate vs actual, surprise %, and the day-of-report intraday price move %. Used by the verdict prompt to anchor 'stock typically moves ±X% on earnings' claims in real numbers. Composite (newswire + market-data). 6h cache.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Path parameter 'ticker' (required). | |
| lookback_quarters | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the data source ('Composite (newswire + market-data)'), caching behavior ('6h cache'), and the specific output fields. It does not explicitly state it is read-only or cover edge cases, but it provides meaningful behavioral context beyond the schema.
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, front-loaded with the primary function and output fields, followed by usage context and data attributes. Every sentence adds value with no redundancy or 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?
Although there is no output schema, the description lists the key return fields and provides source, cache, and usage rationale. It could mention parameter constraints or limitations (e.g., supported tickers), but it is sufficient for an agent to understand what the tool does and why to use it.
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 50%—only ticker has a description, which is tautological ('Path parameter'). The main description adds meaning for lookback_quarters by referring to 'Last N quarters', but it does not elaborate on ticker format or parameter constraints beyond what the schema already shows.
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 specifies 'Last N quarters of earnings for a ticker' and lists the exact fields returned (report date, EPS estimate vs actual, surprise %, intraday price move %). This distinguishes it from sibling tools like tengu_v3_earnings_next or tengu_v3_intel_street_estimates.
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 states it is 'Used by the verdict prompt to anchor... claims in real numbers', giving a clear context for when to use this tool. It does not explicitly name alternatives or provide when-not-to-use guidance, but the intended use case is well-defined.
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.
