LIVE US stock/equity quote from Financial Modeling Prep, by ticker OR company name (e.g. 'AAPL' or 'Apple'): price, % change, market cap, exchange, day + 52-week range, volume. Use for any public-company / stock / ticker price question. This is the stocks equivalent of token_price — NOT for crypto tokens.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | a ticker symbol or company name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It states the data source (Financial Modeling Prep), that it's LIVE, and lists returned fields (price, % change, market cap, exchange, day + 52-week range, volume). It lacks details on error handling or rate limits, but covers the core behavioral expectations.
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 information-dense but every sentence contributes: function, data fields, usage context, and differentiation. It is well-structured and front-loaded with the core purpose, with no 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-parameter tool with no output schema, the description is complete: it covers the query format, data returned, and scope (US stocks). Users have enough information to select and invoke the tool correctly without additional context.
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 input schema already has 100% coverage with a description for 'query', so baseline is 3. The description adds value by providing concrete examples ('AAPL' or 'Apple') and clarifying that company names are valid inputs, which enriches the schema's basic description.
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 tool's function with a specific verb ('LIVE US stock/equity quote') and resource ('stock/equity from Financial Modeling Prep'). It enumerates the data fields returned and distinguishes itself from sibling tools by explicitly positioning as the stocks equivalent of token_price.
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 when-to-use guidance ('Use for any public-company / stock / ticker price question') and an exclusion ('NOT for crypto tokens'). It also names an alternative tool (token_price) as the equivalent for crypto, giving clear usage boundaries.
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.
