Topic-filtered headlines (earnings, analysts, dividend, mergers, acquisition, ipo, fda, guidance, stock_buyback, insider, lawsuit, esg, crypto, and more), optionally per ticker, over a date_range (default last7days, 50 items). Call it when the user asks about a specific event type — 'any FDA news?', 'recent M&A headlines'.
| Name | Required | Description | Default |
|---|---|---|---|
| items | No | ||
| topic | Yes | ||
| ticker | No | ||
| date_range | No | last7days |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses defaults (last7days, 50 items) and the optional ticker filter, which adds context beyond the schema. However, it does not describe the output format, whether results are ordered, or any access requirements. For a read-only news tool this is minimal but acceptable, warranting a mid-range score.
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 with front-loaded purpose. The topic list is dense but conveys the range of event types, and the usage example is succinct. Every phrase earns its place; no redundant or filler content.
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 no output schema and a crowded sibling set of news tools, the description provides enough to select the tool correctly: purpose, usage trigger, parameter semantics, and defaults. It lacks explicit differentiation from related tools like 'tengu_v3_news_category' or 'tengu_v3_news_aggregated', but the event-type focus is clear. A small gap is not describing the response structure, but this is acceptable for a simple headlines tool.
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 is the only source of parameter meaning. It explains 'topic' via examples, 'ticker' as optional per-ticker, 'date_range' with a default, and 'items' implicitly via '50 items'. This compensates well for the schema's lack of descriptions, though it could be more explicit about 'items' as a separate parameter.
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 'Topic-filtered headlines' and lists a variety of event types, distinguishing it from other news tools in the sibling list. The verb 'filtered' plus the resource 'headlines' makes the purpose specific and actionable, and the examples ('any FDA news?', 'recent M&A headlines') reinforce the tool's role.
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 gives explicit guidance: 'Call it when the user asks about a specific event type'. This provides clear context for use. However, it does not mention when not to use it or explicitly name alternative news tools, so it stops short of a 5.
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.
