Live FX mid prices (15-min upstream refresh): pass pairs for specific quotes (e.g. EUR-USD,GBP-USD) or base for all pairs vs one currency (base=USD). Call this when the user asks where a currency pair is trading right now. 30s cache.
| Name | Required | Description | Default |
|---|---|---|---|
| base | No | ||
| pairs | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It reveals a 15-minute upstream refresh and a 30-second cache, which is critical for recency expectations, and specifies 'mid prices'. It doesn't cover output format or error behavior, but for a straightforward price quote tool this is a good level of transparency.
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, front-loaded with the core function and refresh cadence, then usage and trigger. Every word earns its place, with no redundant filler.
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 simple two-parameter schema, no output schema, and the explicit use-case trigger, the description is quite complete. It explains inputs and when to call. It could mention the response format, but 'Live FX mid prices' strongly implies current price data, so the gap is not severe.
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 schema has 0% description coverage, but the description compensates by explaining both parameters: pairs for specific quotes with a hyphenated format example, and base for all pairs vs one currency. It adds clear meaning beyond the bare string type, though it doesn't detail edge cases like both parameters being used together.
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 states the tool provides live FX mid prices, distinguishes it from the many forex news sibling tools by focusing on price quotes, and includes concrete examples like 'EUR-USD,GBP-USD' and 'base=USD'. This is a specific, actionable purpose.
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 explicitly says 'Call this when the user asks where a currency pair is trading right now', which is a clear trigger. It also explains the two usage modes (pairs and base). It does not name alternatives or exclusion cases, but the guidance is sufficient for a simple lookup tool.
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.
