Latest fused signals across all voters — the combined signal after voter aggregation, up to limit names (default 100). Call this when the user asks what the system's current signals are overall; use signals_mtf for one ticker's timeframe alignment.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It does mention the limit parameter's effect ('up to limit names') and implies a read-only query, but it does not explicitly declare it as safe, state any permissions, or describe the return format beyond 'names'. This is adequate but not rich; a 3 reflects the basic disclosure without depth.
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 two concise sentences, front-loaded with the core purpose, then providing usage guidance and a sibling alternative. Every word serves a purpose; no fluff or repetition. This is exemplary structure for a tool description.
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 tool's simplicity (single optional parameter, no output schema), the description covers the essential aspects: what it does, when to use it, and the parameter's semantics. It lacks explicit return format details (e.g., array of objects, fields), but for a list-like retrieval tool, the description is mostly complete. A 4 is appropriate because it handles the low complexity well but leaves minor gaps (ordering, data shape).
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 provides only type/default/range for the single 'limit' parameter, with no description (schema coverage 0%). The description compensates by explaining the parameter's meaning: 'up to limit names (default 100)' and implies the default. However, it does not define what 'names' refers to (e.g., tickers), leaving slight ambiguity. This is strong compensation for the schema gap.
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 retrieves the latest fused signals across all voters, with a specific scope ('combined signal after voter aggregation'). It distinguishes itself from the sibling tool signals_mtf by noting the alternative use case. The verb 'Call this when' and resource 'system's current signals' make the purpose unambiguous.
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 use this tool: 'Call this when the user asks what the system's current signals are overall.' It also provides an alternative: 'use signals_mtf for one ticker's timeframe alignment.' This meets the highest standard for usage guidance.
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.
