Single deal / financing round by deal_id: deal size, type, VC round, pre/post-money valuation, and a synopsis. Call this when the user asks about a specific round ('what was the Series C?'); use deal_relations for the investors, lenders, and tranches behind it.
| Name | Required | Description | Default |
|---|---|---|---|
| deal_id | Yes | Path parameter 'deal_id' (required). |
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 what data is returned and implies a read-only lookup, but does not mention error handling, authorization, data freshness, or behavior when deal_id is invalid. This is a moderate disclosure for a simple lookup tool.
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, front-loaded with the core purpose, and zero wasted words. The first sentence states the function and output; the second provides usage context. This is ideal conciseness.
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 low complexity (single ID lookup, no output schema, no annotations), the description is nearly complete. It covers the operation, key data fields, when to use, and distinguishes the sibling. It omits how to obtain the deal_id, but that can be inferred from other tools. This is a strong, near-complete description.
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 input schema has 100% coverage with a minimal description ('Path parameter deal_id (required)'). The tool description adds that deal_id identifies a specific deal, which is helpful context but does not explain its format, source, or allowed values. This meets the baseline for schema-heavy coverage without further compensation.
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 retrieves a single deal or financing round by deal_id, listing specific data fields (deal size, type, VC round, pre/post-money valuation, synopsis). It also distinguishes itself from the sibling tool deal_relations by explicitly naming the alternative for investors/lenders/tranches.
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?
Provides explicit when-to-use guidance: 'Call this when the user asks about a specific round' with a concrete example ('what was the Series C?'). Also gives an explicit alternative: 'use deal_relations for the investors, lenders, and tranches behind it.' This makes the selection criteria clear.
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.
