Multi-mode web search in parallel across link, cited-synthesis and social-sentiment coverage. Returns synthesized_answer, social_sentiment, citations, and a deduplicated list of links. Call this BEFORE relying on model knowledge for time-sensitive questions (markets, earnings, regulation, breaking news, macro events). 15-min LRU cache.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| query | Yes | ||
| freshness | No | ||
| providers | No | links,synthesis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it does well by disclosing the 15-min LRU cache—a critical behavioral trait for time-sensitive data—and listing return fields. It does not mention rate limits, failure modes, or provider-specific behavior, but the cache disclosure adds meaningful value beyond the 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 compact and front-loaded: first sentence states purpose, second states return values, third provides usage guidance and cache behavior. Every sentence earns its place, with no filler or redundancy.
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?
For a moderate-complexity tool with no annotations and no output schema, the description covers purpose, returns, usage timing, and caching. However, it leaves the freshness and providers parameters underspecified, which is a gap given the lack of an output schema to further clarify behavior.
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, but it explains none of the four parameters. The freshness enum (pd/pw/pm) is cryptic and unexplained, and providers is only weakly hinted at by the phrase 'link, cited-synthesis and social-sentiment coverage.' This leaves non-obvious parameters undefined.
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 uses a specific verb ('search') with a clear resource (web) and characterizes it as multi-mode parallel search across link, cited-synthesis, and social-sentiment coverage. It enumerates concrete return values and the multi-mode framing distinguishes it from single-mode siblings like tengu_v3_research_synthesis and tengu_v3_research_x_sentiment.
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 gives explicit when-to-use guidance: 'Call this BEFORE relying on model knowledge for time-sensitive questions (markets, earnings, regulation, breaking news, macro events).' However, it does not name alternative tools or explicitly state when not to use it, so it falls 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.
