Curated news articles for one ticker from the fundamentals market-data feed, with start_date/end_date filtering (default 50). Call it for ticker-scoped headlines while working inside fundamentals; it is distinct from the primary news surface — use the news tools for broad or breaking coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ticker | Yes | ||
| end_date | No | ||
| start_date | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It reveals that the tool returns curated articles from a specific feed, is scoped to a single ticker, supports start_date/end_date filtering, and has a default limit of 50. This provides meaningful behavioral context, though it omits output format and potential error handling.
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 and front-loaded with the core function. It wastes no words, and every clause adds value by clarifying source, scope, filtering, and usage distinctions.
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?
The tool has no annotations and no output schema, so the description must fully cover what an agent needs. It provides purpose, usage, and filtering guidance, but does not describe the return structure (e.g., fields, ordering) or error behavior, leaving a notable gap for effective use.
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?
Schema description coverage is 0%, so the description must compensate. It mentions start_date/end_date filtering and the default 50, but does not explain the date format, the meaning of 'limit' beyond the default, or the expected ticker format. The ticker itself is implied by the tool name and first sentence, but coverage is only partial.
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 returns 'Curated news articles for one ticker from the fundamentals market-data feed' with date filtering and a default limit. It explicitly distinguishes itself from the primary news surface and news tools, making the purpose and scope 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?
The description provides explicit guidance: 'Call it for ticker-scoped headlines while working inside fundamentals' and tells the agent to 'use the news tools for broad or breaking coverage.' This clearly states when to use the tool and names the alternative tool category.
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.
