Recent 13F position snapshots (alternative-data, cross-fund/cross-ticker, last ~10k rows): fund, ticker, shares, value_usd, report_period — the institutional-ownership signal. Call this when the user asks 'which funds hold X?' or 'what does fund Y own?'; filter by ticker and/or fund (substring match).
| Name | Required | Description | Default |
|---|---|---|---|
| fund | No | ||
| limit | No | ||
| ticker | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses several key behaviors: returns recent snapshots, limits to roughly 10k rows, supports substring matching on ticker and fund, and provides alternative-data institutional ownership. It does not mention pagination or ordering, but that is a minor gap for a simple lookup tool.
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 output fields, followed by a concrete use-case and filtering guidance. Every phrase adds value with no filler or restatement of the tool name.
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?
Despite no output schema, the description compensates by listing returned fields and explaining row-cap and filtering semantics. It does not discuss how this tool relates to sibling sec13f tools or the exact behavior of the limit parameter, but the core query scenario is well covered and sufficiently complete for an agent to use 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?
The schema has no parameter descriptions, so the description adds meaning by explaining that ticker and fund are filters with substring matching. While the limit parameter is not elaborated, its name and schema constraints (default 50, max 500) make it self-explanatory, and the description covers the two most behaviorally important filters.
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 identifies the tool as retrieving recent 13F position snapshots and enumerates the output fields (fund, ticker, shares, value_usd, report_period). It differentiates from related tools by emphasizing 'cross-fund/cross-ticker' and 'last ~10k rows', and ties the purpose to concrete user queries like 'which funds hold X?' and 'what does fund Y own?'.
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 provides explicit usage triggers with natural-language examples, making it clear when to invoke this tool. It does not explicitly name alternative tools such as sec13f_changes or sec13f_history, but the focus on 'recent snapshots' and 'cross-fund/cross-ticker' implicitly differentiates its scope.
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.
