v1_stock_quoteStock quote: Delayed quote & key stats for an equity/ETF/index. Source: Yahoo Finance. $0.005 per call · GET /v1/stock-quote
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Ticker symbol. Example: 'AAPL'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context beyond that: the quote is delayed rather than real-time, the data source is Yahoo Finance, and there is a per-call cost. This gives an agent important expectations that annotations alone do not provide.
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 a single compact sentence that front-loads the core function and includes the most decision-relevant details: delay, asset scope, source, and cost. The leading 'Stock quote:' phrase is slightly redundant with the title but does not materially waste space.
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?
This is a simple one-parameter, read-only tool with no output schema. The description adequately conveys what an agent needs: what it returns, for which instrument types, from what source, and at what cost. It does not enumerate all 'key stats' but that level of detail is not necessary for selecting or invoking 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?
The input schema fully documents the only parameter, 'symbol', with a description and example. The tool description adds no additional parameter-level meaning, so the baseline of 3 applies for high schema description 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 identifies the tool's output as a delayed quote with key stats and scopes it to equity/ETF/index instruments. It does not use an explicit verb like 'get' but the meaning is unambiguous and the asset-class scope helps distinguish it from crypto, FX, and economic data siblings.
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?
Usage context is implied through 'equity/ETF/index' and the Yahoo Finance source, suggesting it is for stock and fund quote lookups. However, it does not explicitly state when not to use it or mention alternatives like v1_crypto_price or v1_fx, leaving the routing decision partly to inference.
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.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.