FULL private-company profile by company_id: financials (revenue/EBITDA/EBIT/net income/EV/net debt), complete financing history (round size/valuation/date/type), classification, HQ/contact, parent hierarchy, and cikcode/ticker to join public data. Call it after resolving the id via search_suggest for the deep dive on one company.
| Name | Required | Description | Default |
|---|---|---|---|
| company_id | Yes | Path parameter 'company_id' (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 clearly lists what data will be returned (financials, financing history, classification, HQ/contact, parent hierarchy, and cikcode/ticker) and indicates a read-only lookup. It stops short of stating error behavior, rate limits, or authentication requirements, but for a simple ID-based fetch it is largely 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 sentences long, with the first sentence front-loading the tool's purpose and contents, and the second providing clear usage direction. Every word adds value; there is no fluff or repetition of schema information.
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 has one required parameter and no output schema, the description adequately communicates what the tool returns (enumerated data categories) and how to use it (after search_suggest). It provides enough context for an agent to select and invoke it correctly among the many sibling tools.
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 describes company_id only as a required path parameter, but the description adds crucial context: it is the identifier resolved via search_suggest and is used for the deep dive on one company. This helps the agent know where to obtain the parameter value and confirms it must be a single company ID.
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 starts with 'FULL private-company profile by company_id', which clearly identifies the action (retrieve a comprehensive profile) and the resource (a private company identified by company_id). It enumerates specific data areas (financials, financing history, classification, HQ/contact, parent hierarchy, cikcode/ticker), making it distinct from sibling tools like company_deals or company_investors.
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 the call sequence: 'Call it after resolving the id via search_suggest for the deep dive on one company.' This tells the agent when to use this tool (after ID resolution, for a single-company deep dive) and implies it is the comprehensive profile tool compared to narrower private market tools.
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.
