Does Sugra API MCP expose 1,500 separate MCP tools?
No. It uses a compact gateway architecture. Agents search and describe the endpoint catalog, then call the required data endpoint through a small, stable set of MCP tools.
@Sugra Systems, Inc.
基本信息
Official MCP server for the Sugra API, giving AI agents source-attributed access to 1,500+ endpoints across 36 data domains from 160+ primary sources. Covers markets, economics, company fundamentals, government, news, cl

使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"sugra": {
"command": "sugra-api-mcp",
"env": {
"SUGRA_API_KEY": "sugra_xxx_yourkey..."
}
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Sugra API MCP is the official Model Context Protocol server for the Sugra API. It gives AI agents source-attributed access to 1,500+ endpoints across 36 data domains from 160+ primary sources through a compact set of MCP tools.
Coverage includes market data, macroeconomics, company fundamentals and filings, government data, news, commodities, climate, weather, maritime intelligence, international trade, and entity screening.
Sugra API MCP is available in two deployment modes:
Tool responses can include source attribution, data freshness, request-cost information, and quota metadata, helping agents produce grounded answers and manage API usage.
Add the following URL as a custom MCP connector in your client:
https://app.sugra.ai/mcp
Authenticate through OAuth where supported, or provide a Sugra API key as a bearer token:
Authorization: Bearer sugra_xxx_yourkey...
The hosted service requires no Python installation and includes all 11 tools.
Install the package:
pip install sugra-api-mcp
Add the server to your MCP client configuration:
{
"mcpServers": {
"sugra": {
"command": "sugra-api-mcp",
"env": {
"SUGRA_API_KEY": "sugra_xxx_yourkey..."
}
}
}
}
The local package runs over stdio by default and exposes the eight core gateway tools.
Sugra API MCP works with Anthropic Claude, OpenAI ChatGPT and GPT-based agents, Google Gemini, xAI, Cursor, VS Code, Zed, Cline, Continue, Windsurf, and custom agents built with the Python or TypeScript MCP SDK.
fetch_data - Select and call an appropriate Sugra API endpoint from a natural-language data request.search_endpoints - Search the Sugra endpoint catalog by subject, source, domain, or capability.describe_endpoint - Retrieve endpoint parameters, schemas, source information, and usage details.call_endpoint - Call a specific Sugra API endpoint with explicit parameters.list_toolsets - Browse available data domains and toolsets.list_sources - Discover primary institutions and data sources available through Sugra.sugra_entity_screen - Screen entities using sanctions, compliance, and identity data.sugra_entity_lookup - Search for and retrieve structured entity information.resolve_entity - Resolve an organization, security, or other entity to a canonical identity.get_snapshot - Build a structured multi-source snapshot for an entity or subject.get_timeseries - Retrieve and normalize time-series data for analysis.Compare inflation, interest rates, labor-market indicators, exchange rates, commodities, and central-bank policy across countries.
Example:
Compare inflation and central-bank policy rates across the G7.
Combine company fundamentals, filings, market prices, ownership data, news, and macroeconomic context.
Example:
Build a current research snapshot for this company, including valuation, fundamentals, recent filings, and material news.
Resolve company identities and screen suppliers, customers, counterparties, vessels, and beneficial owners.
Example:
Screen this supplier and resolve its LEI and sanctions identity.
Compare valuations, fundamentals, market performance, flows, and macroeconomic exposure across industries.
Example:
Compare energy and technology sector valuations and capital flows.
Analyze weather, climate indicators, vessel activity, ports, shipping, trade flows, and related economic data.
Example:
Assess weather and maritime factors affecting this commodity trade route.
Identify which datasets, institutions, and endpoints are available for a research question before running an analysis.
Example:
What fixed-income data is available, and which institutions provide it?
No. It uses a compact gateway architecture. Agents search and describe the endpoint catalog, then call the required data endpoint through a small, stable set of MCP tools.
The hosted MCP runs over Streamable HTTP, requires no local installation, and includes 11 tools. The local Python package runs over stdio or self-hosted HTTP and includes the eight core gateway tools.
A Sugra account or API key is required for authenticated data requests. The hosted endpoint can also use OAuth in supported MCP clients.
Sugra API MCP works with clients supporting standard MCP stdio or Streamable HTTP transports, including Claude, ChatGPT, Gemini-based agents, xAI-based agents, Cursor, VS Code, Zed, Cline, Continue, and Windsurf.
The official repository is: ```text https://github.com/Sugra-Systems/sugra-api-mcp ```
Yes. The MCP server code is released under the MIT License. Sugra API access remains subject to the applicable service plan, quota, and API terms.

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