Static company profile for one ticker — sector, industry, CIK, exchange, market cap and employee count. Call it to know what a company is and how big it is before deeper analysis. Not the XBRL corpus — that discovery lives at /fundamentals/companyfacts/{ticker}.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
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 that the profile is 'static' and enumerates the contained fields, which effectively communicates a read-only, snapshot-like behavior. While it doesn't discuss auth, rate limits, or data freshness, the static nature of the tool is a meaningful behavioral trait for a simple lookup.
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 includes only essential additional context. The second sentence adds valuable exclusion guidance without any fluff. 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?
Given there is no output schema, the description does well to enumerate the returned fields. It provides usage context, distinguishes from the XBRL corpus, and explains the input parameter. It could mention data freshness or that it returns a one-off snapshot, but 'static' already implies this. For a low-complexity single-parameter tool, this is complete enough.
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 0% since the description doesn't repeat the parameter schema, but it compensates well by saying 'for one ticker' and listing the returned profile attributes. The only parameter, 'ticker,' is self-explanatory from the tool name and description, and the description adds the notion that this is a single-ticker operation, clarifying scope.
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 a specific verb and resource: 'Static company profile for one ticker' followed by an explicit list of fields (sector, industry, CIK, exchange, market cap, employee count). It also distinguishes itself from the XBRL corpus by pointing to the alternative endpoint, making it unmistakable what this tool does.
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 clear usage context: 'Call it to know what a company is and how big it is before deeper analysis.' This implies a pre-analysis step and gives a clear 'when to use.' It also excludes the XBRL corpus, which serves as a partial alternative reference. It does not name a specific sibling tool but the guidance is sufficient.
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.
