Company corporate-event history from a licensed events feed (41.9M events, 1990-2026): M&A, guidance changes, buybacks, exec changes, activism, offerings, index adds/drops + 100 more types, newest-first dated headlines + summaries. PRIMARY tool for 'what happened at COMPANY'; filter type= (see /api/v3/events/types), since/until.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| limit | No | ||
| since | No | ||
| until | No | ||
| ticker | Yes | Path parameter 'ticker' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the data source (licensed feed), date range (1990-2026), ordering (newest-first), and output content (dated headlines + summaries), which are useful behavioral traits. However, it does not mention response format, pagination, error behavior, or rate limits, though for a read-only history tool this is reasonably transparent.
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 purpose and followed by usage positioning and filter hints. Every clause adds value: data source, size, event examples, output format, primary use case, and filter parameters. No wasted words.
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 moderate complexity (5 params, no output schema, no annotations), the description covers the essential context: data scope, event taxonomy, ordering, output style, and filtering. It does not describe the exact response structure or pagination behavior, but it is sufficient for an agent to understand what the tool returns and when to invoke it. The completeness is strong but not exhaustive.
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 low (20%), so the description compensates by explaining the meaning and filtering usage of 'type', 'since', and 'until' ('filter type=..., since/until'). It does not explicitly describe 'limit' or 'ticker', but 'ticker' is self-evident from the context and 'limit' is standard. The description meaningfully adds semantics beyond the bare schema.
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 that this tool provides company corporate-event history from a licensed feed, enumerating event types (M&A, guidance changes, buybacks, etc.) and output format (newest-first dated headlines + summaries). It also explicitly differentiates itself as the 'PRIMARY tool for what happened at COMPANY', which distinguishes it from news or other event-related siblings. While it lacks a strong imperative verb, the intent is 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 positions this as the primary tool for a specific use case ('what happened at COMPANY') and gives concrete guidance on filtering by type, since, and until. It does not explicitly name alternatives or state when not to use the tool, but the 'PRIMARY' designation provides strong situational context. The pointer to /api/v3/events/types for valid type values is helpful.
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.
