Fetch retail sales data for financial analysis and market insights using Alpha Vantage API.
Fetch retail sales
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "retail_sales"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| datatype | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Fetch' implies a read operation, but it doesn't disclose any behavioral traits: no rate limits, authentication needs, data freshness, error conditions, or output format. This is inadequate for a tool with zero annotation coverage.
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?
Extremely concise with two words, front-loaded with the core action. There's no wasted text, though this brevity contributes to underspecification rather than clarity.
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?
Given no annotations, 0% schema coverage, no output schema, and a vague purpose, the description is incomplete. It doesn't help the agent understand what the tool does, how to use it, or what to expect, making it inadequate for effective tool selection and invocation.
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%, with one undocumented parameter 'datatype'. The description adds no parameter information—it doesn't explain what 'datatype' means, valid values, or how it affects the fetch. With low coverage and no compensation, this fails to provide meaningful semantics.
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 'Fetch retail sales' states a basic action and resource, but it's vague about scope (e.g., time period, region, metrics) and doesn't distinguish from many sibling tools that also fetch financial/economic data (e.g., cpi, gdp, unemployment). It avoids tautology but lacks specificity.
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 on when to use this tool versus alternatives. With 100+ sibling tools including economic indicators like cpi and gdp, the description provides no context, prerequisites, or exclusions, leaving the agent to guess based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.