Clustered FX news events from a forex newswire — high press-coverage stories like central-bank decisions, rate hikes, and intervention rumors, optionally filtered to one pair (e.g. EUR-USD). Call this when the user asks 'what major macro events hit today' or what's moving a currency. 300s cache.
| Name | Required | Description | Default |
|---|---|---|---|
| pair | No | ||
| items | No | ||
| date_range | No | today |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It does add useful behavioral context like the '300s cache' and the 'optionally filtered to one pair' flexibility. However, it does not disclose the response format, pagination behavior, or data freshness limitations, leaving notable gaps for a tool with no output schema.
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 that each add value: the first defines the tool's content and clustering aspect, the second gives concrete usage triggers, and the third notes the cache time. It is free of fluff and efficiently front-loads the most important information.
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 purpose and basic usage but omits essential contextual details. Without an output schema, it fails to explain what the returned data looks like (e.g., event objects, fields). It also does not differentiate from the many similar forex news tools in the sibling list, making selection ambiguous in some scenarios.
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 0%, so the description must compensate for all parameters. It explicitly explains 'pair' with an example (EUR-USD) and indirectly implies 'date_range' through 'today', but 'items' is entirely undocumented. With three parameters and only partial coverage of two, the compensation is insufficient.
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 specifies a distinct type of content: 'Clustered FX news events from a forex newswire' with concrete examples like central-bank decisions, rate hikes, and intervention rumors. It clearly identifies the resource (forex news events) and key characteristics (clustered, high press-coverage), distinguishing it from siblings like forex_latest or forex_economic_calendar.
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 states when to call it: when the user asks about 'major macro events today' or what's moving a currency. This is clear contextual guidance, though it does not mention alternatives or when not to use the tool, 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.
