Management guidance history — every company-issued guidance range (measure, period, low/high, announce date, street consensus at that date) from the analyst-estimate Guidance archive. Call it to compare what management promised vs what the street expected, or to study guidance-cut reactions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| start | No | ||
| ticker | Yes | Path parameter 'ticker' (required). | |
| measure | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It describes the data scope ('every company-issued guidance range') and provides context about the content, but it does not disclose potential limitations such as pagination, data freshness, or whether all guidance types are covered. It also does not explicitly state that it is a read-only operation, though that is implied. This is adequate but not rich.
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, front-loaded with the core concept ('Management guidance history') and immediately followed by the detailed contents. The second sentence provides actionable use cases. There is no redundant or filler text, and every phrase adds value.
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 lack of output schema and annotations, the description does help by enumerating the data fields and use cases. However, it leaves key implementation details unexplained, such as how to filter by 'measure' or 'start', and what the 'limit' parameter controls. This is a fairly simple query tool, but the missing parameter guidance creates a gap for agents trying to invoke it correctly.
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 25% (only ticker is described). The description does not explain the optional parameters 'limit', 'start', or 'measure'. It lists 'measure' as a field in the response, but does not clarify that it is also a query parameter. With low schema coverage, the description needed to compensate but did not, leaving agents without guidance on how to use these parameters effectively.
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 identifies the resource as 'Management guidance history' and specifies the exact content: 'every company-issued guidance range (measure, period, low/high, announce date, street consensus at that date)'. It distinguishes this tool from sibling tools like tengu_v3_intel_street_estimates by emphasizing 'company-issued' and the 'Guidance archive', making it clear this is about management guidance, not analyst 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 provides explicit use cases: 'Call it to compare what management promised vs what the street expected, or to study guidance-cut reactions.' This clearly indicates when to use the tool. However, it does not explicitly mention when not to use it or recommend alternative tools (e.g., street_estimates), so it falls short of a 5.
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.
