Returns top-performing hedge fund managers ranked by success rate and returns.
Args:
num: Number of managers to return (default 10, max 100 — the upstream
returns 100 for anything higher)
| Name | Required | Description | Default |
|---|---|---|---|
| num | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey that the tool is read-only, idempotent, and non-destructive. The description adds genuinely useful behavior beyond annotations: the 'num' parameter is capped at 100, and the upstream returns 100 for anything higher. It also clarifies the ranking criteria, which helps set expectations about output ordering.
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 and well-organized: one sentence stating the tool's purpose and ranking criteria, followed by a short Args section documenting the only parameter. Every sentence earns its place with 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?
For a single-optional-parameter, read-only list tool with rich annotations, the description is complete enough to call correctly. It covers what the tool returns, how results are ranked, and the parameter's default and limits. Output schema is absent, but the return values are reasonably inferable and not needed to invoke the tool.
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 carry the meaning for the 'num' parameter. It does so excellently by explaining that num is 'Number of managers to return' and documenting the default, maximum, and the upstream clamping behavior. This fully compensates for the missing schema descriptions.
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 uses a specific verb and resource: 'Returns top-performing hedge fund managers ranked by success rate and returns.' This clearly distinguishes it from related hedge fund tools like get_hedge_fund_holdings and get_hedge_fund_activity, which focus on holdings and activity rather than manager performance.
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 clearly implies when to use this tool: when you need a ranked list of top hedge fund managers based on success rate and returns. It does not explicitly name alternatives or state when not to use it, 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.
Many tools have overlapping purposes, e.g. get_etf_analysis vs get_etf_forecast both provide ETF analyst consensus, get_etf_holdings vs get_etf_top_stocks both list constituents, and get_portfolio_overview vs get_portfolio_performance both return returns/performance. The detailed descriptions help, but the sheer number of similar tools creates ambiguity in selection.
The set is largely consistent with a 'get_' prefix and descriptive nouns (get_stock_quotes, get_crypto_quote, get_dividend_history). Minor deviations include 'list_my_portfolios' instead of 'get_my_portfolios' and singular/plural variants like get_all_commodities_quotes vs get_commodity_quote, but the pattern remains predictable.
With 71 tools, the count far exceeds the 50+ threshold described as an extreme mismatch. Even though the server covers a broad financial domain, such a large surface is overwhelming for an agent and includes many redundant or highly specific tools that could be consolidated.
The tool set provides comprehensive coverage of TipRanks data: quotes and historical data for all major asset classes, news, earnings and economic calendars, analyst and sentiment data, financial statements, technical analysis, options, portfolios, and screeners. There are no obvious dead ends for typical financial research tasks.