Search earnings-call transcripts. scope=headlines (default) searches call titles across ALL companies; scope=text searches the spoken words inside one company's transcripts (ticker REQUIRED) and returns per-call hit counts + a snippet. Use when the user asks 'which calls mention X' — then fetch the full text with the returned event_id.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| limit | No | ||
| scope | No | headlines | |
| since | No | ||
| ticker | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that headlines mode searches across all companies, text mode requires a ticker and returns per-call hit counts plus a snippet. However, it does not disclose behaviors around `limit`, `since`, or pagination, and the return format is only partially described.
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 dense sentences, front-loaded with the core action. Every clause adds value with no wasted words, making it highly concise and well-structured.
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 the two usage modes, the required parameter for text mode, and the next step (fetch full text with event_id). It is missing detail on `since` and `limit`, and no output schema exists, but for a search tool the description is sufficiently complete for common usage.
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. It explains `scope` (headlines vs text) and `ticker` (required for text), and `q` is implied as the search query. However, `limit` and `since` are not explained, leaving gaps in parameter understanding.
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 it searches earnings-call transcripts and explicitly distinguishes between headline search (across all companies) and text search (within one company's transcripts). It also differentiates from the sibling transcript_text tool by instructing to fetch full text with the returned event_id.
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?
It explicitly states when to use the tool: when the user asks 'which calls mention X' and then instructs to fetch the full text with the returned event_id. It also clarifies scope selection and the ticker requirement for text mode, guiding correct usage and next steps.
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.
