Connects AI agents to Sugra’s catalog of market, economic, company, government, climate, maritime, news, and screening data.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Sugra API.
sugra_entity_screenScreen a person or organization name against the Sugra sanctions corpus. Returns a SCREENING SIGNAL, not a compliance determination. Sugra is a technology provider, not a sanctions authority or consumer reporting agency. PEP and adverse-media coverage is supplementary and non-comprehensive - a `clear` result is not proof of absence, and a `hit` is a candidate match to review, not a finding. Output is COMPACT to protect the agent context budget: `{status, matches:[{name, score, list, type}], disclaimer}`. The verdict `status` is one of `clear`, `review`, or `hit`. The heavy raw fields (match rationale, source ids, publish dates) are dropped; use the Sugra API directly when the full screening envelope is needed. Args: name: The person or organization name to screen (required). country: Optional ISO 3166-1 alpha-2 country to narrow the match. dob: Optional date of birth (YYYY-MM-DD) for a person. nationality: Optional nationality to narrow the match.
sugra_entity_lookupResolve an entity by identifier and return its composed KYB envelope. `anchor` is `lei` (Legal Entity Identifier, resolved via the GLEIF registry) or `vat` (EU VAT number, validated via the EU VIES service). The result weaves identity, a sanctions screening signal, and - on request - ownership and adverse-media slices. The screening verdict is a SCREENING SIGNAL, not a compliance determination, and any PEP / adverse-media content is supplementary and non-comprehensive. The `disclaimer` field carries this and is always present. Output is COMPACT by default to protect the agent context budget: `{entity:{name, anchor, value, status, country}, screening:{status, top_matches:[...3], hit_count}, ids:{...}, disclaimer}`. Pass `include` to opt INTO fuller per-slice detail, e.g. `include=["ownership","adverse_media"]` adds those slices in full form. On a bad anchor or an API error this returns a clean `{error, detail}` dict rather than raising, so the agent can branch on `result.get("error")`. Args: anchor: Identifier type, one of `lei` or `vat`. value: The identifier value (the 20-char LEI code or the VAT number). include: Optional list of fuller slices to add, e.g. `["ownership", "adverse_media"]`. Omit for the compact default.
search_endpointsSearch the bundled Sugra endpoint catalog by natural-language query. Use this to pick an operation_id. It does not fetch data. Typical loop: 1. search_endpoints(query) -> ranked hits with required_parameters 2. describe_endpoint(operation_id) -> params, request_body_schema, agent_hints 3. call_endpoint(operation_id, params=..., body=...) or fetch_data(query, params=...) Filter with toolset or source only after list_toolsets / list_sources; a misspelled filter is an error, not a silent empty result. Examples: - search_endpoints("US CPI inflation") - search_endpoints("AAPL price", toolset="markets") - search_endpoints("container ship AIS", toolset="network")
describe_endpointDescribe one Sugra API endpoint by operation_id. Includes agent_hints (duration_class fast/slow/heavy, max_concurrency, bulk billing) so you can budget timeouts and parallelism before calling. POST endpoints with a JSON body also carry request_body_schema (the resolved JSON schema) - construct the `body` argument from it instead of guessing key names. Call this after search_endpoints and before call_endpoint when you need the exact parameter names and examples.
call_endpointCall a Sugra API endpoint by operation_id from the bundled catalog. Plan calls with describe_endpoint's agent_hints: duration_class "fast" usually responds in under ~2s, "slow" usually 1-5s and occasionally 15s+ on a cold upstream, "heavy" can exceed the gateway timeout - keep parallel calls within max_concurrency and prefer small batches. Bulk endpoints bill 1 request credit per body item. Failures return structured errors {error, reason, status_code, elapsed_ms, retry_hint}; after "upstream_timeout" a single retry often succeeds because the aborted attempt warms upstream caches.
list_toolsetsList catalog groups with endpoint counts and short descriptions. Use the group names as the toolset filter on search_endpoints. This does not call the Sugra API; it reads the bundled catalog.
The Sugra API MCP server gives MCP-compatible agents access to Sugra’s data gateway and its bundled endpoint catalog. The catalog covers more than 1,600 endpoints across markets, economics, companies, government, news, climate, maritime, and entity screening. It is intended for agents that need to find relevant data operations, call them with structured inputs, and preserve attribution and freshness details in the result.
The server also includes composed workflows for entity and market research. An agent can resolve free text such as a ticker, company, macro indicator, coin, or currency pair to a canonical entity; request a current snapshot using a named recipe; or retrieve bounded historical data for supported metrics. Entity tools support sanctions and watchlist screening, as well as lookup by LEI or VAT identifier.
Discovery starts with the local catalog. search_endpoints, describe_endpoint, list_toolsets, and list_sources can inspect available operations, groups, source metadata, parameters, required inputs, and JSON request schemas without fetching /openapi.json. fetch_data combines endpoint discovery and execution for a natural-language request. call_endpoint executes a selected operation by its operation_id; arbitrary path calls are not supported.
Only actual data operations contact the Sugra API. Results include structured metadata such as source attribution, freshness, and rate-limit cost, allowing an agent to report where values came from and account for quota usage. Composed snapshot requests use a fixed recipe cost of one or two requests from the daily quota, while time-series responses include an explicit indication when downsampling was applied.
For local use, install the Python package with pip install sugra-api-mcp and provide an API key through SUGRA_API_KEY. The local package runs over stdio for desktop clients and IDEs, and it can also serve through Streamable HTTP for self-hosting. A free account and key are available from Sugra; the documented free tier allows 50 requests per day.
The hosted endpoint is https://mcp.sugra.ai/mcp, with https://app.sugra.ai/mcp listed as a permanent alias. Hosted connections can authenticate through OAuth in the Claude and ChatGPT connector flows or with a bearer API key. Claude, ChatGPT, and Codex can add the hosted server through their respective directory or connector flows; other MCP clients can use the hosted Streamable HTTP endpoint.
The Sugra API MCP server exposes gateway and composed tools including:
operation_id.The repository also describes five MCP resources containing agent skills and six workflow prompts for supported clients. These prompts guide recurring tasks such as macro research, market snapshots, sanctions screening, sector comparison, climate and maritime research, and source discovery; they do not add separate MCP tools.
The server depends on the Sugra API for live data calls, so an API key or hosted authentication is needed for authenticated use. Catalog inspection is local, but endpoint execution consumes request quota. Ambiguous entity resolution returns ranked candidates rather than silently selecting one. ETF flow metrics have different meanings: etf_flows is estimated at filing cadence, while etf_monthly_flows represents the fund’s NPORT-P monthly creations and redemptions. Data availability depends on the selected catalog operation and its source.
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