One-shot fundamentals bundle for a ticker — metrics snapshot, TTM income, latest balance sheet and cash-flow, company facts, recent insider trades and top institutional holders, fetched in parallel. PRIMARY tool for 'give me the full fundamental picture of X' — call it instead of assembling the pieces one by one.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 conveys that data is fetched in parallel and returned as a single bundle, listing the exact data categories. However, it does not explicitly state that this is a read-only operation with no side effects, nor does it disclose potential limitations like partial data availability for illiquid tickers. Still, the behavioral traits disclosed (parallel fetch, bundle composition) are useful and go beyond the name.
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 enumerates the bundle contents and the parallel fetch behavior, the second delivers the primary-use directive. Every word earns its place, no filler, and the key information is front-loaded.
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 tool with one parameter and no output schema, the description covers the essential context: what data is included and when to use it. It could mention that the response is read-only or that some bundles may lack data for certain tickers, but given the simplicity and list of components, the description is nearly complete.
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 already documents the single 'ticker' parameter (100% coverage), so the baseline is 3. The description only reiterates 'for a ticker' and adds no extra meaning about formatting (e.g., ticker symbol vs. company name), case sensitivity, or accepted exchanges. It essentially relies on the schema's minimal description.
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 states a specific action ('One-shot fundamentals bundle for a ticker') and enumerates exact included components (metrics snapshot, TTM income, balance sheet, cash-flow, company facts, insider trades, institutional holders). It clearly distinguishes itself from sibling tools that fetch individual pieces, making the purpose unambiguous.
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?
Explicitly identifies the primary use case: 'PRIMARY tool for "give me the full fundamental picture of X"' and instructs to call it instead of assembling pieces individually. This provides clear when-to-use guidance and implicitly excludes scenarios where only a specific data slice is needed.
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.
