Sentiment leaderboard across the full tracked ticker universe over a date_range (default last7days), paginated. Call this when the user asks which stocks have the most positive or negative news sentiment right now — a screening pass before single-name digs.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| date_range | No | last7days |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses that the result is paginated, covers a date range (default last7days), and spans the full tracked universe, which is useful context. However, it omits response structure, sort order, accepted date_range values, and page size, leaving significant behavioral gaps.
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 purpose, followed by a concise usage statement. Every word earns its place; 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 simple tool with only two optional parameters and no output schema, the description adequately covers purpose, scope, and usage context. It could elaborate on the exact contents of the leaderboard (e.g., ticker symbols, sentiment scores), but the term 'sentiment leaderboard' plus the screening use case makes it reasonably complete for an agent to select and invoke.
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 compensate. It mentions 'date_range (default last7days)' and 'paginated', but these largely restate schema defaults and do not explain what values are valid for date_range, how pagination works (e.g., page size), or the effect on results. This is insufficient compensation for zero schema-level descriptions.
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 defines the tool as a sentiment leaderboard across the full tracked ticker universe, with explicit scope (full universe), time window (date_range), and pagination. It distinguishes itself from sibling tools by framing its use as a 'screening pass before single-name digs', making its purpose unmistakable.
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 when to call it ('when the user asks which stocks have the most positive or negative news sentiment right now') and implies a boundary ('before single-name digs'), but it does not name alternative tools for single-name deep dives, so it falls short of the full 'explicit when/when-not/alternatives' bar.
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.
