Full US Congress roster (House + Senate, alternative-data) with disclosed trade counts per member. Call it to resolve a politician name to a BioGuideID before pulling their trades, or for 'most-active disclosed traders in Congress' lists. Heavy full-roster pull.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that this is a 'Heavy full-roster pull,' which is a critical behavioral warning about load/performance. It also clarifies the data scope (House + Senate, alternative-data). It doesn't detail response size or rate limits, but for a simple read-only roster pull, this is sufficient.
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, front-loaded with the core purpose, and each clause adds value: what it returns, use cases, and a warning. No filler 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?
The description covers the main functionality and warns about the heavy pull, but it leaves the `limit` parameter ambiguous and provides no output schema or detail on the return format. For a simple one-parameter tool without annotations, this is a gap in completeness, making it only barely adequate.
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 one parameter `limit` with no description, and the tool description doesn't explain it. The phrase 'Full US Congress roster' conflicts with a default limit of 100 (less than the full 535 members), creating confusion about whether the limit truncates the roster or applies only to the 'most-active' list. This ambiguity is not addressed, so the description fails to compensate for the missing schema description.
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 provides a 'Full US Congress roster (House + Senate, alternative-data) with disclosed trade counts per member.' This is a specific verb+resource and distinguishes it from sibling tools like tengu_v3_intel_congress by emphasizing the roster and ID resolution purpose.
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 gives explicit usage scenarios: 'resolve a politician name to a BioGuideID before pulling their trades' and for 'most-active disclosed traders in Congress' lists. It also warns this is a 'Heavy full-roster pull,' implying it should be used with caution. However, it doesn't explicitly name the alternative tool for pulling trades, 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.
