Congressional stock trades from two coverage sources — a realtime cross-ticker feed (provider=options_flow, default) or a bulk alternative-data feed (provider=alternative_data) — with an optional ticker filter. Call this when the user asks what Congress members have been buying or selling, market-wide or in a specific name.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ticker | No | ||
| provider | No | options_flow |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context by distinguishing a 'realtime cross-ticker feed' from a 'bulk alternative-data feed' and noting the default provider. However, it does not discuss output format, pagination, data coverage limitations, or any potential side effects, leaving gaps for an agent to discover at runtime.
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 the main subject and scope front-loaded. Every clause adds value: data source, provider options, default, ticker filter, and explicit usage context. No fluff or redundancy.
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 has no annotations and no output schema, the description reasonably covers purpose, usage, and two of three parameters. Yet it omits the meaning of 'limit' and does not describe the shape of the returned trade records (e.g., fields like date, amount, party), which would be useful for an agent to set expectations. It is adequate for selection but slightly incomplete for full invocation understanding.
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 has no property descriptions (0% coverage), so the description must compensate. It does so by explaining the provider parameter with its enum values ('options_flow' realtime, 'alternative_data' bulk) and by clarifying that ticker is an optional filter. However, the limit parameter is not explained in the description, leaving its purpose to be inferred from the schema's min/max/default values.
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 congressional stock trades from two named coverage sources, with an optional ticker filter. It identifies the specific resource (congressional stock trades) and implicitly the function (retrieval), distinguishing it from sibling tools like tengu_v3_intel_politicians by focusing on trade transactions.
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 explicitly says 'Call this when the user asks what Congress members have been buying or selling, market-wide or in a specific name.' This gives a clear trigger condition. However, it does not mention when not to use it or name alternative tools for related queries (e.g., political contribution data), so it lacks explicit exclusions or alternatives.
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.
