Funding-round and M&A deal history for a private company (deal size, type, VC round, pre/post-money valuation), newest first. Call this when the user asks 'when did X last raise / at what valuation / who acquired it'; use /investors for who participated.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| company_id | Yes | Path parameter 'company_id' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that results are sorted newest first, include fields like deal size and valuation, and that investor participation is excluded. It does not cover edge cases like empty results or error behavior, but for a straightforward read-only history tool, the disclosure is solid.
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?
Two sentences: the first defines the purpose and content, the second gives usage and an alternative. Everything is front-loaded and each sentence adds value without fluff.
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?
Despite having no output schema or annotations, the description explains what is returned (deal size, type, VC round, valuation), how it is ordered (newest first), and when to use it. It also names the alternative for investors. This is complete for a simple history endpoint with clear parameters.
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 covers one parameter (company_id) with a description, while limit is self-explanatory via type/default/max/min. The description implicitly requires company_id but does not explain it or the limit parameter explicitly. At 50% coverage, the description neither compensates nor adds confusion, so a baseline 3 is appropriate.
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's purpose: "Funding-round and M&A deal history for a private company... newest first." It specifies the resource (private company deals), the verb (retrieve history), and the scope (funding rounds and M&A), distinguishing it from sibling tools that focus on investors or company profiles.
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?
Usage is explicitly tied to user intents: "Call this when the user asks 'when did X last raise / at what valuation / who acquired it'." It also provides a clear exclusion and alternative: "use /investors for who participated." This directly tells when to use this tool versus a sibling.
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.
