Monthly time series of ONE factor characteristic for a stock (e.g. mom_12m, bm, mktcap, realized_vol, sue, turnover), newest first. Call it to chart how an anomaly signal evolved for a name or to compare signal drift across names; a typo in char returns 422 with the full list of valid column names.
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| char | No | ||
| limit | No | ||
| start | No | ||
| ticker | Yes | Path parameter 'ticker' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden. It discloses ordering ('newest first'), the single-characteristic constraint ('ONE factor characteristic'), and a specific error behavior (typo in `char` returns 422 with valid column names). It does not mention date-range defaults or pagination, but the disclosed traits are useful and beyond what the schema provides.
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?
Two sentences deliver the core data definition, use case, and error behavior with no wasted words. The structure is front-loaded with the resource and scoping, making it easy to scan.
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 description covers the essential purpose, use cases, ordering, and error handling. Given the tool's simplicity and no output schema, it is fairly complete. Minor gaps remain around start/end parameter semantics and limit behavior, but those are inferable from schema names and defaults.
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 low (20%), so the description must compensate. It adds meaning for `char` by listing example values and the 422 error condition, and `ticker` is self-evident from the name. However, start/end/limit are not explained beyond their schema names and defaults, leaving important semantics (e.g., date format, range limits, default behavior) to inference.
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 the resource: 'Monthly time series of ONE factor characteristic for a stock', with concrete examples (mom_12m, bm, mktcap, etc.) and ordering ('newest first'). It also implicitly distinguishes from sibling tools by using '_history' and explicitly from the non-history variant through the singular 'ONE factor characteristic' and use cases for charting evolution.
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 provides concrete when-to-use guidance: 'Call it to chart how an anomaly signal evolved for a name or to compare signal drift across names'. However, it does not explicitly name alternative tools or state when not to use it, leaving room for ambiguity.
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.
