crypto-news: daily sentiment rollup for a crypto symbol (-1.5 to +1.5). Densest single-call signal — N days of (positive_count, negative_count, neutral_count, sentiment_score). Use to detect sentiment regime shifts on BTC/ETH/etc. 300s cache.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | ||
| date_range | No | last30days |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It discloses the output structure, the sentiment score range, the single-call nature, and the 300s cache. It does not specify read-only semantics or how the date_range parameter maps to N days, but for a simple data-retrieval tool this is a solid disclosure.
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 compact sentences plus a short cache fragment, front-loading the essential purpose and outputs. Every word earns its place, with no filler or repetition of schema fields.
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?
Even without an output schema, the description provides enough context for the agent to understand the return shape (per-day counts and sentiment_score) and the tool's niche. It is missing a precise link between date_range and N days, and does not mention default behavior for the date range, but overall it is a strong description for a simple 2-parameter tool.
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 coverage is 0%, so the description must explain parameters. It clarifies that ticker is a crypto symbol (via 'for a crypto symbol' and 'BTC/ETH/etc.'), but it never names or explains date_range, and 'N days' is ambiguous as to whether it is controlled by the date_range parameter. The enum values are left entirely to the schema, so the description adds minimal beyond the schema.
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 a daily sentiment rollup for a single crypto symbol, lists the exact output components (positive_count, negative_count, neutral_count, sentiment_score), and defines the score scale (-1.5 to +1.5). It distinguishes itself from sibling news tools by emphasizing 'Densest single-call signal' for one symbol, versus all-tickers or market-wide sentiment tools.
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 a clear use case: 'Use to detect sentiment regime shifts on BTC/ETH/etc.' This tells the agent when to invoke the tool. However, it does not explicitly mention when not to use it or name alternative tools for multi-ticker or market-wide sentiment, so it falls short of a full 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.
