general MCP Server
Sugra MCP: connector between LLM agents and world data. 1,400+ endpoints aggregating 130+ primary sources across 32 data domains: markets, macroeconomics, company fundamentals, government, news, climate, maritime, trade, and entity screening. Works with Anthropic Claude, OpenAI GPT, Google Gemini, xAI, and any MCP-enabled client.
Discovered via github-topic:model-context-protocol and last synced 3mo ago.
1. Install the package
pip install sugra-api-mcp
No (HTTP)
List catalog groups with endpoint counts and descriptions.
Required
No
Purpose
Yes (stdio)
HTTP + OAuth
No (HTTP)
Search the bundled endpoint catalog. Runtime search does not fetch `/openapi.json`.
No
One-step: find best endpoint for a natural-language query and call it. Combines search + call in one round trip.
No
Show bundled catalog source metadata.
HTTP + OAuth
Inspect an endpoint by `operation_id`, including path, method, parameters, required inputs, `agent_hints`, and `request_body_schema` for JSON-body POST operations.
Call a Sugra API operation by `operation_id`. Arbitrary path calls are no longer supported.
Screen a name against sanctions and watchlists (Sugra Entity).
Composed entity lookup by identifier - `anchor` is `lei` or `vat`, plus the identifier `value`; returns registry identity + screening (Sugra Entity).
Free text (ticker, company, indicator, coin, currency pair) to a canonical market or macro entity. Ambiguous matches return ranked candidates, never a silent pick.
Entity plus a named recipe to one composed current view with freshness, provenance, coverage, and billing blocks. Composed calls charge a fixed recipe cost (1-2 requests) from the daily quota.
Entity plus metric (`price`, `macro_series`, `etf_flows`) to a bounded series with an explicit downsampling flag.
Retry strategy
No response within `SUGRA_TIMEOUT` (`elapsed_ms` close to `timeout_s` x 1000)
Could not reach the Sugra API (DNS failure, connection refused)
Connection dropped mid-request
Unexpected failure inside the gateway (`exception_type` included)
Implementing and auditing GCP VPC firewall rules to enforce network segmentation, restrict ingress and egress traffic, apply hierarchical firewall policies across the organization, and monitor firewall rule effectiveness using VPC Flow Logs.
Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.
Configuring Google Cloud Identity-Aware Proxy (IAP) to enforce per-request identity verification for Compute Engine, App Engine, Cloud Run, and GKE services using access levels, context-aware policies, and programmatic access with service accounts.
Implement GCP Binary Authorization to enforce deploy-time security controls that ensure only trusted, attested container images are deployed to Google Kubernetes Engine and Cloud Run.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
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