Daily Twitter mention volume and follower count for one ticker from the alternative-data feed (default 60 days). Call this when the user asks how much social buzz a name has or whether attention is spiking; pair with tengu_v3_intel_wsb for the r/wallstreetbets read.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ticker | Yes | Path parameter 'ticker' (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 adds context by naming the data source ('alternative-data feed'), daily granularity, and the default 60-day window. However, it does not disclose the response structure, potential data gaps, rate limits, or explicitly state that it is a read-only operation. The added context is useful but not exhaustive.
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 entire description is two sentences with no filler. The first sentence states functionality and default behavior, while the second provides usage context and a sibling recommendation. Every word earns its place.
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 simple two-parameter data retrieval tool with no output schema, the description covers what data is returned (mention volume, follower count), the source, default window, and intended use case. It omits the exact return format (e.g., time series structure), but the mention of 'daily' implies a time-based series and the described metrics are sufficient for an agent to set expectations.
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 coverage is 50% (ticker is described, limit is not). The description compensates by clarifying that the tool works for one ticker and by stating 'default 60 days', which implies the limit parameter controls the lookback window. It does not explicitly name the limit parameter, but together with the schema's min/max, the meaning is inferable.
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 opens with 'Daily Twitter mention volume and follower count for one ticker', clearly specifying the verb (get), resource (Twitter alternative-data), and scope (one ticker). It effectively distinguishes itself from sibling tools by naming Twitter-specific metrics and explicitly referencing tengu_v3_intel_wsb as a separate WSB-focused tool.
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 call the tool ('when the user asks how much social buzz a name has or whether attention is spiking') and recommends pairing with tengu_v3_intel_wsb for WallStreetBets. It does not provide an explicit 'when not to use' statement, but the context is clear enough to avoid misrouting.
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.
