Curated end-of-day 'sundown' digest — a market-close recap of the day's news from the newswire, over an optional date_range. Call this when the user asks 'what happened in the market today' or wants a daily wrap-up.
| Name | Required | Description | Default |
|---|---|---|---|
| date_range | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions 'curated' and 'market-close recap' giving some behavioral context, but it does not disclose the default date_range behavior (e.g., what happens when date_range is omitted) or the output format (whether it returns headlines, summaries, or market movers). This leaves the agent guessing about important behavioral traits.
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 then usage guidance. Every word earns its place, with no unnecessary detail or repetition.
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 simplicity (1 optional param, no output schema), the description provides a decent big picture. However, it leaves gaps around parameter format, default behavior, and expected response content. An agent would struggle to know how to properly request a specific date range or understand the digest output without further assumptions.
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 has one parameter, date_range, with no description, and schema description coverage is 0%. The description only says 'over an optional date_range' without specifying the expected format (e.g., YYYY-MM-DD), range syntax, or default value. This adds minimal meaning beyond what the schema and required-parameter count already imply.
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 identifies it as a 'Curated end-of-day sundown digest — a market-close recap of the day's news from the newswire', which is a specific verb+resource+scope. It distinguishes itself from siblings like tengu_v3_news_latest or tengu_v3_news_summary by emphasizing the curated, market-close nature.
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?
It explicitly states when to use the tool: 'Call this when the user asks what happened in the market today or wants a daily wrap-up.' This provides clear context, though it does not mention alternatives like the crypto or forex sundown digests that might be more appropriate for those specific market segments.
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.
