Private-market landscape aggregates grouped by sector, industry_group, region, or country: company counts, total and median capital raised, median valuation, median employees. Call this for market-level questions like 'which sectors raise the most' — not for single companies.
| Name | Required | Description | Default |
|---|---|---|---|
| by | No | sector | |
| limit | No |
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 discloses the metrics returned (company counts, capital raised, median valuation, median employees) and the aggregation behavior, but it also states the tool is for market-level questions. However, it doesn't explicitly state that the operation is read-only, nor does it mention pagination, data coverage, or any potential limitations. The description adds value but lacks deeper behavioral context.
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 long, highly information-dense, and front-loaded with the core purpose. The example ('which sectors raise the most') illustrates usage without wasted words. Every sentence 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 aggregation tool with no output schema, the description covers the essential aspects: purpose, grouping dimensions, metrics, and usage boundary. The main missing piece is the 'limit' parameter explanation, but the tool is otherwise simple enough that the description is nearly complete for selecting and invoking it correctly.
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 must compensate. It effectively documents the 'by' parameter by listing its enum values (sector, industry_group, region, country). However, the 'limit' parameter is completely omitted, which is a significant gap given there are only two parameters. The description partially compensates but doesn't fully cover all parameters.
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 private-market aggregates with specific metrics (company counts, capital raised, median valuation, median employees) and grouping dimensions (sector, industry_group, region, country). It explicitly distinguishes from single-company tools with 'not for single companies', making the purpose unambiguous and distinct from siblings.
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?
Provides explicit when-to-use guidance: 'Call this for market-level questions' and an exclusion: 'not for single companies.' While it doesn't name an alternative tool, the guidance is clear and actionable. It could be improved by naming the single-company tool, but the boundary is well-defined.
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.
