Analyst rating actions — upgrades, downgrades, and initiations, filterable by tickers, rating_type, and date_range; history goes back to 2022-04-08. PRIMARY tool for 'any recent upgrades or downgrades on X?' and for gauging how sell-side conviction is shifting.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| items | No | ||
| tickers | No | ||
| date_range | No | ||
| rating_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It discloses the history start date (2022-04-08) and the filter dimensions, providing some context about data scope. However, it doesn't mention output format, pagination behavior, or any side effects, leaving notable gaps.
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 a single, well-structured sentence that front-loads the core definition and adds a usage pointer. No wasted words.
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 provides the essential selection criteria: what the tool returns, its filter dimensions, history depth, and primary use case. The lack of an output schema and format details for tickers/date_range are not fully compensated, but the tool's straightforward nature makes this sufficient for selection and invocation.
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?
The description mentions three of the five parameters (tickers, rating_type, date_range) and their role as filters, which adds meaning over the bare schema. However, it doesn't specify format for tickers or date_range, and page/items are not mentioned at all. This partial coverage is reasonable but not comprehensive.
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 tool as covering 'Analyst rating actions — upgrades, downgrades, and initiations' and specifies it is filterable by tickers, rating_type, and date_range. This clearly distinguishes it from sibling tools like credit ratings or general news by naming the specific resource. The verb is implicit but the resource is exact.
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?
Explicitly states it is the 'PRIMARY tool' for queries like 'any recent upgrades or downgrades on X?' and for gauging sell-side conviction, giving a clear when-to-use. It doesn't mention alternatives or when not to use, but the primary designation is sufficient.
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.
