sugra-api-mcp

Give any AI agent access to 1,500+ data endpoints across markets, economics, companies, government, news, climate, maritime and entity screening - through one MCP server.

Works with ChatGPT, Claude, Gemini, xAI, Cursor, VS Code and any MCP client.

Official Model Context Protocol server for the Sugra API: one connector, a bundled endpoint catalog, and structured tool results with source attribution on every answer.

See it in action

An agent answering a real question end to end - resolving entities, pulling live snapshots and history, and citing the source and freshness on every number:

More examples:

Macro research - one prompt builds a full G7 inflation and policy-rate table, each cell dated and sourced, with the unavailable ones flagged rather than faked:

Cross-domain snapshot - Brent crude, marine weather and regional risk pulled together for a shipping desk, each with its source and timestamp:

Related MCP server: BlockRun MCP

What a session looks like

Hosted MCP transcript (the three composed tools shown here run on the hosted endpoint). Captured example - wording and figures vary by run and as new BLS data is published:

User: Where does US inflation stand, and how has it trended over the past year?

resolve_entity("US inflation")
  -> macro indicator cpi_us (U.S. Bureau of Labor Statistics)
get_snapshot("cpi_us")
  -> latest reading with freshness, provenance and quota cost
get_timeseries("cpi_us", metric="macro_series", range="1y")
  -> 12 monthly points with an explicit downsampling flag

Agent: US CPI printed 2.9% year over year in the latest release, down from
3.5% twelve months earlier - a steady decline since spring.
Source: U.S. Bureau of Labor Statistics via the Sugra API.

Every tool result carries structured metadata - source attribution, freshness, and rate-limit cost - so agents can cite sources and budget requests instead of guessing.

How it works

flowchart LR
    A["AI agent<br/>(ChatGPT, Claude, Gemini, xAI, IDEs)"] --> B["Sugra MCP<br/>hosted: 11 tools / local: 8 tools"]
    B --> C["Sugra API<br/>1,500+ endpoints, 36 data domains"]
    C --> D["160+ primary sources<br/>markets, economics, government,<br/>news, climate, maritime"]

Behind the gateway sits the Sugra API: 160+ primary sources - sovereign statistics agencies, central banks, intergovernmental bodies and more - feeding 1,500+ endpoints across 36 data domains. The server ships a bundled catalog of the full endpoint surface, so discovery (search, describe, toolsets) runs locally without network calls; only actual data requests hit the API.

What agents build with it

Six workflow prompts ship with the server and turn these into one-click flows in clients that surface MCP prompts:

• Market and macro research - "Compare inflation and central bank policy rates across the G7." (macro_briefing)

• Equity snapshots with sources - "Where does NVIDIA stand today - price, profile, and market backdrop?" (market_snapshot)

• Sanctions and compliance screening - "Screen this supplier and resolve its LEI identity." (sanctions_screening)

• Sector comparison - "Energy versus technology: valuations and flows side by side." (sector_compare)

• Climate, maritime and trade intelligence - "Red Sea shipping this week: chokepoint transits, crude price, and weather on the route." (earth_conditions plus the transport and commodities catalog)

• Source discovery - "What does the catalog offer for fixed income, and from which institutions?" (source_overview)

Every answer carries source attribution and freshness metadata, so agents cite instead of guessing.

Hosted MCP (recommended)

No install. Point your client at the hosted Streamable HTTP endpoint:

https://app.sugra.ai/mcp

• 11 tools: the eight gateway tools plus three composed agent tools (resolve_entity, get_snapshot, get_timeseries)

• OAuth sign-in through the claude.ai and ChatGPT connector UIs, or Authorization: Bearer sugra_xxx_... with an API key

• In claude.ai: Settings -> Connectors -> Add custom connector

• In ChatGPT: Settings -> Connectors -> Add MCP server

Local package

Runs on your machine over stdio (or self-hosted HTTP) with an API key:

pip install sugra-api-mcp

• Eight gateway tools

• stdio for desktop clients and IDEs, Streamable HTTP for self-hosting

• Authenticates with SUGRA_API_KEY

Get a free API key at app.sugra.ai/settings/billing (Free tier: 50 req/day).

Quick start

pip install sugra-api-mcp
export SUGRA_API_KEY=sugra_xxx_...   # free key: app.sugra.ai/settings/billing
sugra-api-mcp call quotes_symbol_price --params '{"symbol":"AAPL"}'

The same call through an agent: connect the server to your client (next section) and ask "What is AAPL trading at? Use Sugra." The agent finds quotes_symbol_price in the catalog and calls it with the symbol.

Connect your client

Supported clients:

• Anthropic Claude: Claude Desktop, Claude Code (CLI), claude.ai (web)

• OpenAI GPT: ChatGPT (via MCP connector)

• Google Gemini: Gemini CLI, Gemini Code Assist (VS Code + JetBrains)

• xAI: Remote MCP Tools in xAI SDK and Responses API

• IDEs: VS Code (native), Cursor, Zed, Cline, Continue.dev, Windsurf

• Custom agents: anything built on the Python or TypeScript MCP SDK

Claude Desktop (stdio)

Add to claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: Claude Desktop has no Linux build. On Linux, pip install sugra-api-mcp and use Claude Code (CLI), an IDE client, or the hosted HTTP endpoint below.

{
  "mcpServers": {
    "sugra": {
      "command": "sugra-api-mcp",
      "env": {
        "SUGRA_API_KEY": "sugra_xxx_yourkey..."
      }
    }
  }
}

Restart Claude Desktop. Sugra tools appear in the tools menu.

Claude Code (Anthropic CLI)

claude mcp add sugra -- sugra-api-mcp
# then set the env var that sugra-api-mcp reads
export SUGRA_API_KEY=sugra_xxx_...

Or edit ~/.claude/config.json manually with the same shape as Claude Desktop above.

Cursor, Zed, Cline, Continue.dev, Windsurf

Each of these has an MCP settings file (typically mcp.json or equivalent) with the same stdio config shape as Claude Desktop.

ChatGPT

ChatGPT supports MCP through its connector UI. Use the hosted HTTP endpoint (below) since ChatGPT does not launch local stdio processes.

HTTP (claude.ai, ChatGPT, remote agents)

Hosted Streamable HTTP endpoint:

https://app.sugra.ai/mcp

Add to claude.ai, ChatGPT, or any Streamable HTTP MCP client. Authenticate with Authorization: Bearer sugra_xxx_....

In claude.ai: Settings -> Connectors -> Add custom connector. In ChatGPT: Settings -> Connectors -> Add MCP server.

Tool reference

The local package exposes eight gateway tools. The hosted endpoint adds three composed analysis tools on top (see Hosted MCP above). The package exposes exactly eight tools:

call_endpoint and fetch_data both support response shaping with limit, fields, and include_raw. Shaping works on enveloped ({"data": ...}) and envelope-less payloads alike; fields entries may use dotted paths into nested objects (geo.city), and meta.shaped reports what was actually applied (fields_applied / fields_unmatched, limit_applied) rather than echoing the request.

describe_endpoint returns computed agent_hints per endpoint so agents can budget time and parallelism before calling:

• duration_class - fast (under ~2s, snapshot-backed), slow (live upstream proxying, occasionally 15s+), or heavy (per-item upstream work, large batches can exceed the gateway timeout)

• max_concurrency - advisory ceiling for parallel calls from one session

• bulk_cost - on per-item bulk endpoints: 1 request credit per item in the request body (the API reports the total in the X-RateLimit-Cost response header)

Hosted-only agent tools (app.sugra.ai/mcp)

The hosted MCP endpoint at https://app.sugra.ai/mcp serves the same eight tools PLUS three composed agent tools that are not available on stdio or self-hosted installs:

These three tools wrap an internal composed plane that requires an infrastructure credential available only on the hosted deployment. The tool code ships inside the package, but it is registered only by the hosted HTTP entry point and only when that credential is present - pip install sugra-api-mcp (stdio and self-hosted HTTP) always exposes the classic eight-tool gateway. Hosted-only examples in any documentation are labeled as such. For compliance entity lookups (LEI / VAT, sanctions screening) use sugra_entity_lookup and sugra_entity_screen, which work on every transport.

CLI

Server startup is unchanged:

sugra-api-mcp
sugra-api-mcp --transport streamable-http --port 8001

Catalog and gateway helpers:

sugra-api-mcp doctor
sugra-api-mcp list-toolsets
sugra-api-mcp search "NASDAQ futures"
sugra-api-mcp describe cot_financial
sugra-api-mcp call quotes_symbol_price --params '{"symbol":"AAPL"}'

Environment variables

HTTP transport with OAuth

When running with --transport streamable-http the server allows unauthenticated MCP discovery requests (initialize, notifications/initialized, tools/list, resources/list, prompts/list, and ping) so ChatGPT Apps and other mixed-auth clients can discover tool metadata. CORS is enabled for major MCP clients (ChatGPT, Claude, Cursor) so browser connector UIs can complete the OAuth flow; override the allowlist with SUGRA_MCP_ALLOWED_ORIGINS. Tool calls still require Authorization: Bearer .... Two token formats are accepted:

• Raw API key (sugra_...) - passed through as the downstream x-api-key. Compatible with earlier local API-key setups.

• OAuth JWT - signature verified against the issuer's JWKS. The audience must match https://app.sugra.ai/mcp, the token must include sugra:read, and hosted access is validated against APP before resolving the user's primary API key. Successful hosted OAuth requests update MCP connection activity in APP.

Timeouts and the error contract

SUGRA_TIMEOUT caps each downstream HTTP call from this server to the Sugra API (default 30 seconds). It is one link in a longer chain; when a tool call fails, elapsed_ms in the error payload tells you which link cut it:

MCP client (agent harness)         own tool timeout, often 60-180s, client-controlled
  -> hosted proxy (app.sugra.ai)   86400s, effectively unlimited
    -> this server (httpx)         SUGRA_TIMEOUT, default 30s
      -> Sugra API -> upstreams    15-60s per upstream call, server-side

Tool failures return structured JSON instead of raising, so agents can pick a retry strategy:

All error payloads carry elapsed_ms. url is present on transport and HTTP errors (not on tool_execution_failed, which can fire before a URL exists). On the three transport errors status_code is null (no HTTP status was received) - consumers comparing status_code numerically should guard for that. If a tool call instead fails with a bare client-side message and no structured JSON, the timeout fired in your agent harness above this server: raise the client's tool timeout, not SUGRA_TIMEOUT.

Examples

Ask Claude:

• "Search Sugra endpoints for NASDAQ futures."

• "Describe the cot_financial operation."

• "Call quotes_symbol_price with symbol AAPL and return only symbol and price."

• "List available Sugra toolsets."

Troubleshooting

Looking for get_market_price, get_macro_indicator, or get_news? Those curated tool names appear in some older directory listings and never shipped in this package - use fetch_data for one-step natural-language calls or search_endpoints plus call_endpoint for explicit routing.

missing_api_key in tool responses

The server starts and lists its tools without a key, but API-calling tools (call_endpoint, fetch_data, the entity tools) return {"error": "missing_api_key"} until the server can find one. Depending on how you run it:

• As an MCP tool from your client (Claude, ChatGPT, Gemini, xAI, IDE, etc.): check the env block in your MCP config file. Value should be a full key like sugra_ao1_..., not empty and not wrapped in extra quotes.

• Shell / CI: export SUGRA_API_KEY=sugra_... before running sugra-api-mcp.

• HTTP mode: set via .env or systemd EnvironmentFile, not the shell.

sugra-api-mcp doctor reports whether the key is visible to the process.

401 Unauthorized or 403 Forbidden in tool responses

Key accepted but rejected. Common causes:

• Key was regenerated in app.sugra.ai/settings/billing and your config still has the old one.

• Typo - key contains only lowercase letters and digits, no spaces, no trailing newlines.

• Free tier was deactivated. Sign in to verify status.

429 Too Many Requests

Hit your plan's daily limit. Response headers include X-RateLimit-Reset with the UTC timestamp when the counter resets (midnight UTC). Upgrade your plan at app.sugra.ai/settings/billing.

Invalid Host header (only if self-hosting HTTP mode)

FastMCP has DNS rebinding protection. Set SUGRA_MCP_ALLOWED_HOSTS to a comma-separated list of the public hostnames your reverse proxy serves. Example: SUGRA_MCP_ALLOWED_HOSTS=mcp.example.com,example.com.

Tool result truncated with meta.truncated notice

Some endpoints return very large payloads (global wildfires, full table catalogs). The client enforces the MCP 25k token limit - when hit, the data list is trimmed and a retry hint appears in meta.truncated.retry_hint. Add narrower filters (country, date range, limit) to get the full result.

Python version 3.11 or higher is required

sugra-api-mcp requires Python 3.11+. Check: python --version. If you have 3.10 or older:

• Ubuntu: install Python 3.11 or newer from your distribution packages or the deadsnakes PPA.

• macOS: brew install python@3.11

• Windows: download from python.org

Then recreate your venv.

Hosted app.sugra.ai/mcp returns 5xx

The hosted endpoint can briefly restart after deploys. Wait 60 seconds and retry. If persistent, email support@sugra.systems.

Debugging tool calls locally

Run with stdio and log JSON-RPC messages:

SUGRA_API_KEY=sugra_... sugra-api-mcp 2>&1 | tee mcp-debug.log

Send manual JSON-RPC from a second terminal using nc or an MCP inspector.

Development

git clone https://github.com/Sugra-Systems/sugra-api-mcp
cd sugra-api-mcp
pip install -e ".[dev,http]"
export SUGRA_API_KEY=sugra_...
python -m sugra_api_mcp  # stdio mode
python -m sugra_api_mcp --transport streamable-http --port 8001  # HTTP mode
python scripts/build_endpoint_catalog.py  # rebuild bundled catalog from sibling API openapi.json

Run tests:

pytest

Docker

Build the image from the repository root:

docker build -t sugra-api-mcp .

Run in stdio mode (the default entrypoint) for MCP clients that spawn a local process:

docker run -i --rm -e SUGRA_API_KEY=sugra_... sugra-api-mcp

Run the Streamable HTTP transport on port 8001 with Docker Compose:

export SUGRA_API_KEY=sugra_...
docker compose up -d

Then point your MCP client at http://localhost:8001/mcp. The compose service passes SUGRA_API_KEY, SUGRA_API_BASE, SUGRA_TIMEOUT, SUGRA_MCP_ALLOWED_ORIGINS, and SUGRA_MCP_ALLOWED_HOSTS through from your shell environment when set, and checks container health against http://localhost:8001/health.

A note on auth: no environment variable is baked into the image and none is required for the container to start. In HTTP mode clients authenticate per request with Authorization: Bearer sugra_..., so SUGRA_API_KEY on the container is only a fallback for requests without a Bearer token (see the environment table above). Behind a reverse proxy, set SUGRA_MCP_ALLOWED_HOSTS to the public hostnames you serve.

License

MIT © 2026 Sugra Systems, Inc.