Api Mcp

Name: Api Mcp
Author: LLM Mart

No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.

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:

Compare NVIDIA, AMD and Intel over the past 12 months, answered live through the Sugra MCP

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:

A G7 inflation and central bank policy rate table assembled live from the Sugra API

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

A Red Sea shipping snapshot combining Brent crude, marine weather and hazard sourcing

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

Five official skills ship as MCP resources (sugra://skills/...) and as SKILL.md files in sugra_api_mcp/skills/: explore the catalog, envelope and attribution, auth and rate limits, hosted vs gateway, and a cross-domain briefing pattern. Install them as a plugin from this repository (next section), or resources/read the matching URI after connect.

Agent skills

These skills teach the catalog loop. They do not add MCP tools. Connect the Sugra MCP server separately (hosted or local).

Claude Code

/plugin marketplace add Sugra-Systems/sugra-api-mcp
/plugin install sugra-api@sugra-api-mcp