fx-news: daily sentiment rollup for an FX pair (-1.5 to +1.5). Densest single-call signal — N days of (positive_count, negative_count, neutral_count, sentiment_score). Use to detect regime shifts on EUR-USD/GBP-USD/etc. 600s cache.
| Name | Required | Description | Default |
|---|---|---|---|
| pair | Yes | ||
| date_range | No | last30days |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the return data structure (N days of positive_count, negative_count, neutral_count, sentiment_score), the sentiment score range, and a 600s cache. This goes beyond basic expectations, though it does not discuss error handling, timezone conventions, or freshness beyond the cache, so it is not a 5.
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 long, front-loaded with the core identity ('fx-news: daily sentiment rollup'), and every sentence contributes value: the data contents, the use case, and the cache. There is no superfluous wording, and the structured notation aids parsing.
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 an output schema and annotations, the description adequately covers the tool's main aspects: what it returns (the four fields), the sentiment scale, the intended use for regime shifts, and caching. It does not provide example output or specify date_range options explicitly, but the essential context is present for a tool with only two parameters.
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 adds meaning for the 'pair' parameter by giving examples (EUR-USD/GBP-USD), which the schema lacks. It references 'N days' without explicitly mapping to the date_range parameter, but the enum values (last7days, last30days, last60days) are self-explanatory. Since schema description coverage is 0%, the description partially compensates but not fully for both parameters.
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 an FX pair, listing the data components and sentiment range (-1.5 to +1.5). It distinguishes itself from siblings by the 'fx-news' prefix and the explicit focus on FX pairs, but it lacks an explicit verb such as 'get' or 'list', relying on the noun phrase 'rollup' to convey the action.
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 regime shifts on EUR-USD/GBP-USD/etc.' This gives the agent a contextual trigger for when to invoke the tool. However, it does not mention exclusions or alternatives, such as when to use a non-FX sentiment tool or a more granular news tool, so it stops 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.
