LIVE top stock movers for the current/most-recent US session from Financial Modeling Prep: biggest gainers, biggest losers, or most-active by volume. Use for 'top movers / top gainers / biggest losers / most active stocks today'. The stock analogue of market_movers.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | how many to return (default 10, max 25) | |
| direction | No | gainers, losers, or actives (default gainers) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses the LIVE nature, US session scope, and data source (Financial Modeling Prep), which is useful. However, it does not mention the response format, rate limits, or explicitly confirm the operation is read-only.
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?
Three concise, information-dense sentences: function, use cases, and sibling distinction. No redundant phrases or fluff.
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?
The tool is simple and parameters are fully documented, but there is no output schema. The description does not state what fields the returned movers contain (e.g., symbol, price, change), which is a notable gap for an AI agent deciding if this tool answers a query.
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 100% with descriptions for limit and direction. The description repeats the direction categories (gainers, losers, actives) but adds no additional semantic detail beyond what the schema already provides, so baseline 3 is appropriate.
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 specifies the tool's function: fetching top stock movers with specific categories (gainers, losers, actives). It explicitly contrasts with market_movers as the 'stock analogue', effectively distinguishing it from sibling tools.
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?
Gives explicit use cases: 'Use for top movers / top gainers / biggest losers / most active stocks today'. Also references market_movers as the alternative, providing clear when-to-use context.
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 share the same core purpose (safety checks) differentiated mainly by chain or asset type, and descriptions are detailed enough to distinguish them most of the time. However, pairs like rug_check/rugcheck and deployer_check/deployer_reputation have overlapping purposes that could lead to misselection.
Most tool names follow a lowercase snake_case pattern with clear descriptors, but there are notable exceptions like 'rugcheck', 'defillama', and 'verify'. Additionally, the 'rug_check' vs 'rugcheck' pair is an obvious naming inconsistency.
44 tools is excessive for any server. Even for a broad security/due-diligence purpose, many tools (especially the robinhood_* series) are highly specialized and could be consolidated into fewer actions.
The tool surface is impressively comprehensive, covering token/NFT safety, transaction simulation, whale/address tracking, stock analysis, prediction markets, and project validation. Minor gaps exist (e.g., no ENS resolution or stock price history), but they are not critical for the server's core purpose.
