이는 Alpha Vantage API에 대한 액세스를 제공하는 MCP 서버로, 주식 데이터 검색을 LLM의 컨텍스트로 사용할 수 있도록 합니다.
getStockQuote : 주식의 현재 견적을 가져옵니다.
getHistoricalData : 주식에 대한 과거 데이터를 가져옵니다(일일, 주별 또는 월별).
(기술 분석, 회사 개요 등을 위한 도구는 나중에 추가될 예정입니다.)
Related MCP server: AlphaVantage MCP Server
Smithery를 통해 Claude Desktop용 Financial Data Server를 자동으로 설치하는 방법:
지엑스피1
npm install findata-mcp-serverAlpha Vantage에서 API 키를 받으세요. https://www.alphavantage.co/support/#api-key .
MCP 클라이언트(예: Claude Desktop)를 구성하여 서버에 연결합니다.
{
"mcpServers": {
"alphaVantage": {
"command": "npx",
"args": ["-y", "findata-mcp-server"],
"env": {
"ALPHA_VANTAGE_API_KEY": "PUT_YOUR_API_KEY_HERE"
}
}
}
}getStockQuote주식의 현재 시세를 알아보세요.
입력:
symbol : 주식 기호(예: AAPL) 출력 예:
getHistoricalData주식에 대한 과거 데이터를 가져옵니다.
입력:
symbol : 주식 기호(예: AAPL)
interval : 데이터의 시간 간격( daily , weekly 또는 monthly )(선택 사항, 기본값: daily )
outputsize : 출력 크기( compact 또는 full )(선택 사항, 기본값: compact ) 출력:
과거 데이터를 포함하는 JSON 객체입니다. 출력 구조는 interval 매개변수에 따라 달라집니다.
기여를 환영합니다! 이슈를 개설하거나 풀 리퀘스트를 보내주세요.
MIT
Get historical data for a stock.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | The stock symbol (e.g., AAPL) | |
| interval | No | The time interval for the data (daily, weekly, or monthly) | daily |
| outputsize | No | The size of the output (compact or full) | compact |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It only states 'Get historical data' without mentioning rate limits, output format, data granularity, or any other behavioral traits, leaving significant gaps for a data-retrieval tool.
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 concise sentence that is easy to parse and front-loaded. While it is minimal, it avoids redundancy and clearly names the primary action and resource, though it could benefit from slightly more detail.
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 the lack of annotations and output schema, the description is incomplete. It does not explain the return format, data types, or any caveats around intervals or output size. A more complete description would clarify what 'historical data' entails and what the response contains.
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 covers all three parameters (symbol, interval, outputsize) with descriptions, so the baseline is 3. The tool description adds no additional parameter context beyond the schema, but it doesn't need to since the schema is self-explanatory.
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: retrieve historical data for a stock. While it doesn't explicitly distinguish from the sibling tool getStockQuote, the word 'historical' implies a contrast with current quote data, making the purpose understandable.
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 gives no guidance on when to use this tool versus alternatives like getStockQuote. It does not mention typical use cases, prerequisites, or exclusions, so the agent must infer usage solely from the name and input schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current quote for a stock.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | The stock symbol (e.g., AAPL) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries full responsibility for disclosing behavior. It only states that it returns a 'current quote' but does not describe the response format, whether it includes fields beyond price, potential delays, authentication requirements, or any side effects. With no output schema, this lack of detail leaves the agent underinformed about what to expect.
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, concise sentence that front-loads the core purpose. It contains no fluff or redundant information, and every word earns its place. It is appropriately sized for the tool's simplicity.
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 low-complexity (one well-documented parameter, no annotations, no output schema). The description adequately states what it does, but without an output schema or any behavioral details, the agent cannot know the full set of fields returned by a 'quote' or handle edge cases. It is minimally viable but leaves clear gaps.
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 single parameter 'symbol' with an example ('AAPL') and a clear description. The tool description adds only the word 'stock,' which provides no substantial new meaning beyond what the schema already states. Since schema coverage is 100%, the baseline of 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 states the tool's purpose: 'Get the current quote for a stock.' It uses a specific verb (Get), names the resource (current quote), and the word 'current' distinguishes it from the sibling tool getHistoricalData. This is unambiguous and effective.
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 gives clear context that this tool is for current quote data, which implies a use case distinct from historical data. However, it does not explicitly mention when not to use it or name the alternative getHistoricalData as a fallback. The context is clear but lacks explicit exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.1getHistoricalDatagetStockQuoteScored across 2 tools
The two tools are clearly distinct: one retrieves historical data and the other retrieves the current quote, with no overlap in functionality.
Both tools follow a consistent verb_noun naming pattern (getHistoricalData, getStockQuote), making them predictable and easy to understand.
With only two tools, the server feels too sparse for a comprehensive financial data API, lacking common operations like search, bulk queries, or parameterized requests.
The tool set is severely incomplete for a financial data server, missing essential functionality such as querying multiple stocks, specifying date ranges, or obtaining metadata, leading to likely dead ends for agents.
MCP server for stocksense-ai documentation, generated by doc2mcp.
MCP server exposing the Backtest360 engine API as tools for AI agents.
Alpha Vantage MCP — Stock market data, fundamentals, and earnings
Financial data MCP server for Claude, ChatGPT, Cursor and Codex. Real-time stock quotes, financial statements, options flow, SEC filings, insider trades, 13F holdings, macro data and market news from gloom.sh, the open-source Bloomberg Terminal alternative.
