Get AI-powered stock analysis for a ticker symbol including price, market cap, P/E ratio, EPS, and analyst assessment. Premium API. Cost: $0.25 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| lookup | Yes | Stock ticker symbol or company name (e.g. 'AAPL', 'COIN') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read operation ('Get... analysis'), but the annotations have destructiveHint=true, creating a direct contradiction. Additionally, the description does not clarify the discrepancy or disclose any other behavioral traits like error handling or rate limits.
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 extremely concise—two sentences covering purpose and cost—with no wasted words. The most critical information (what it does, what it provides) is front-loaded.
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 description lists key outputs but lacks details on error handling, response format, or what happens for invalid tickers. Since there is no output schema, some additional context about the response would be beneficial, but the description is minimally adequate for a simple 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?
The input schema already fully describes the only parameter ('lookup' with description 'Stock ticker symbol or company name'). The description adds no additional semantics beyond listing the outputs, so it meets the baseline for high schema coverage.
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 purpose with a specific verb ('Get') and resource ('AI-powered stock analysis for a ticker symbol'), listing specific data points (price, market cap, etc.). It distinguishes itself from the many other interzoid tools by focusing solely on stock analysis.
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?
No guidance is provided on when to use this tool vs. alternatives. The description only mentions cost and premium nature, but does not specify prerequisites, limitations, or scenarios where other sibling tools would be more appropriate.
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 distinct data operations (matching, standardization, enrichment) with clear descriptions. Minor overlaps exist, e.g., address_match_advanced vs global_address_match, but descriptions differentiate them.
All tools follow a consistent 'interzoid_descriptive_function' pattern in snake_case, making it easy to predict purpose from the name.
58 tools is high for a single server, exceeding the typical 3-15 range. While each serves a specific data enrichment function, the quantity may overwhelm agents without clear categorization.
The tool surface covers a broad domain including address, company, person, and financial data. Minor gaps exist (e.g., no reverse IP lookup, limited social media coverage), but core data needs are well-addressed.
