INSTANT (sub-100ms) private-company typeahead — prominence-ranked with the SAME ranking as search, so the famous company is never truncated; each hit carries authoritative website/domain/logo_url plus sector, last-known valuation, ticker. Call this FIRST to resolve a name to company_id; use /search for multi-entity or detail=full.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses key behavioral traits: sub-100ms performance, prominence ranking with the same ranking as search, non-truncation of famous companies, and the specific fields returned per hit. It does not mention error behavior or rate limits, but the core operational semantics are well exposed.
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 a compact two-sentence block that front-loads the key 'INSTANT' behavior and packs in ranking, fields, and usage guidance without any filler. Every clause adds value and the structure is efficient.
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 2-parameter tool with no output schema or annotations, the description is remarkably complete. It explains the tool's behavior, return field list, ranking rationale, and usage context, making it sufficient for an agent to select and invoke it correctly without further documentation.
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 has no field descriptions (0% coverage), so the description must compensate. It implicitly clarifies 'q' as a company name to resolve via typeahead, but it never explicitly explains the 'limit' parameter or its impact on results. The generic default/min/max in the schema partially conveys meaning, leaving a gap for this simple two-parameter tool.
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 is a private-company typeahead, explicitly distinguishing it from the search tool by noting it resolves a name to company_id first. It specifies the resource (private companies) and the verb (typeahead/search), and contrasts behavior with the sibling /search endpoint.
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 FIRST to resolve a name to company_id' and an explicit exclusion: 'use /search for multi-entity or detail=full.' This names the alternative tool and provides clear boundary conditions for selection.
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.
