List all hedge funds for a user (active + closed).
Args:
user_id: The user's ID.
Returns a JSON array of fund objects with: id, fund_name, is_active,
inception_date, initial_capital, current_aum, router_preset,
aggression_mode, total_pnl_usd, total_pnl_bps, and roster summary.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It clearly states that the tool returns a JSON array and enumerates the fields in each fund object, plus the active+closed inclusion. It could mention error behavior or ordering, but for a simple list operation it is transparent.
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 compact, front-loaded with the core purpose, and organized with Args and Returns sections. Every line provides useful information without redundancy or filler.
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 one-parameter read-only list tool, the description covers the operation, the parameter, and the return shape. It lacks pagination or error-handling details, but those are not essential given the 'all funds' wording and the explicit field list.
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 description names user_id and says it is the user's ID, which adds minimal meaning beyond the schema's required string property. Since schema description coverage is 0%, the description does not fully compensate, but the single parameter is straightforward enough that this is still adequate.
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 verb ('List'), the resource ('all hedge funds'), and the scope ('for a user' with active and closed). This distinguishes it from siblings like get_fund, get_active_fund, and create_fund by describing a collection-level read operation.
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 gives clear context for when to use this tool: to retrieve all hedge funds for a user, including both active and closed funds. It does not explicitly name alternatives or state when not to use it, but the scope is unambiguous.
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.
Most tools target a distinct resource and action, but browse_fund_marketplace and get_marketplace_bots are easy to confuse since both surface marketplace bots with performance metrics. The detailed descriptions clarify their different intents, though the naming does not strongly reinforce the distinction.
The set almost uniformly uses snake_case verb_noun names like create_fund, list_funds, and update_fund_weights. Minor inconsistencies exist, such as get_marketplace_bots returning a list instead of a single item and browse_fund_marketplace using a different pattern from the other marketplace tools.
At 26 tools, the server is on the heavy side and above the typical well-scoped range. The broad domain of funds, strategies, marketplaces, and market data helps justify the count, but several overlapping marketplace/list tools inflate the surface and could be consolidated.
The set covers the core fund lifecycle, roster management, strategy validation/deployment, marketplace browsing, and market data reads quite thoroughly. Minor gaps remain, such as no update/delete operations for strategies and no direct tool for publishing a bot to the marketplace, but these are workable within the existing workflow.