Sugra API MCP server

• call_endpointFree

  Call 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.

• describe_endpointFree

  Describe 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.

• fetch_dataFree

  One-step fetch: find the best Sugra endpoint for the query and call it. Combines search_endpoints + call_endpoint into a single round trip. Use this when you want data without manually picking an operation_id. The full search_endpoints + describe_endpoint + call_endpoint dance is still available when you need explicit control, but for most natural-language queries this tool is enough. Behavior: 1. Search the bundled catalog for the query. Top match wins. 2. If the matched endpoint has required parameters and they are all provided in `params`, call it and return the response. 3. If required parameters are missing, return the candidate endpoints and the missing-params list so the LLM can retry with the correct `params` dict on the next call. Examples: - `fetch_data("US CPI inflation", params={"series_id": "CPIAUCSL"})` → calls /api/v1/fred/series/CPIAUCSL, returns observations. - `fetch_data("Bitcoin price", params={"coin_id": "bitcoin"})` → calls /api/v1/crypto/bitcoin/price. - `fetch_data("Latest financial news")` → news_latest has no required params, returns latest news directly.

• get_snapshotFree

  Composed current view of an entity via a named recipe. Executes a fixed server-side recipe (company_snapshot, etf_snapshot, quote_snapshot, macro_indicator_snapshot, macro_calendar, earnings_snapshot, debt_snapshot) and returns one envelope with freshness, provenance, per-component coverage, and billing. Composed calls charge the recipe's fixed cost (1-2 units) from the daily quota. status "partial" means an optional component was unavailable - the present components are still trustworthy; honor the freshness block (stale=true means the data aged past its budget). Args: recipe: Recipe name from the fixed manifest. entity: Entity dict from resolve_entity ({"namespace": ..., "ids": ...}).

• get_timeseriesFree

  Bounded timeseries for an entity: price, macro_series, etf_flows or etf_monthly_flows. Returns points oldest-first with an explicit downsampling flag when the raw series exceeded max_points. Times are UTC. Costs 1 unit per call. The two ETF flow metrics answer different questions and are not interchangeable. ``etf_flows`` is an ESTIMATE at filing cadence: one point per SEC filing refresh, so ``t`` is a filing date and even a wide window yields a handful of points. ``etf_monthly_flows`` is the fund's own creations and redemptions from its NPORT-P filing, so ``t`` is a calendar month (``YYYY-MM``) and each point carries the three filed components - sales, reinvestment, redemption - beside the net. Two things to read before quoting etf_monthly_flows. NPORT-P is filed per SERIES, so for a fund with more than one share class the figures cover every class and the payload says so in ``multi_class_series``; where the class count is unknown it says ``class_scope`` instead of staying silent. And a fund that files no NPORT-P at all, such as a commodity trust, is not an error: the call returns status ``partial`` with an empty point list and a ``reason``. Args: metric: One of price / macro_series / etf_flows / etf_monthly_flows. entity: Entity dict from resolve_entity ({"namespace": ..., "ids": ...}). granularity: Requested point granularity (default "1d"). max_points: Hard cap on returned points (default 500).

• list_sourcesFree

  List source families in the bundled catalog with endpoint counts. Use the family names as the source filter on search_endpoints. This does not call the Sugra API.

• list_toolsetsFree

  List 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.

• resolve_entityFree

  Resolve free text to a canonical market or macro entity. Turns a ticker, company name, macro indicator, coin, or currency pair into the agent plane's ``{namespace, ids}`` entity for use with get_snapshot and get_timeseries. A cross-namespace collision (e.g. a ticker that is both an equity and a coin) returns status "ambiguous" with ranked candidates and NEVER silently picks one; pass type_hint (e.g. "equity", "etf", "coin") to narrow the universe. Crypto aliases resolve too (e.g. "bitcoin" -> the BTC coin entity). Status "low_confidence" means the best match cleared resolution but scored weakly - verify the returned entity before building on it, or re-query with a more specific name or type_hint. For compliance KYB lookups by LEI/VAT or sanctions screening use sugra_entity_lookup / sugra_entity_screen instead - this tool is for market-data entities. Args: query: Free-form text - ticker, company, indicator, coin, or pair. type_hint: Optional namespace hint narrowing resolution.

• search_endpointsFree

  Search 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")

• sugra_entity_lookupFree

  Resolve 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.

• sugra_entity_screenFree

  Screen 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.

• Code scan42 source files scanned25/25
• Live reliabilityremote reachable in 606ms20/20
• Tool poisoning11 tool descriptions checked15/15
• Auth qualityopen endpoint, read-only tools10/15
• Maintenancelast push 4 days ago15/15
• Maintainer identitynamespace and repository owner differ; GitHub account older than a year6/10

Sugra API: common questions

Is Sugra API MCP server safe?

Yes, by our scan: it is graded A (91/100). Read the Sugra API safety report

How do I install Sugra API?

It runs remotely at app.sugra.ai. Add it to Claude Code, Claude Desktop or Cursor with the snippets above, or call it through the mcp.market gateway without installing anything.

Does Sugra API need an API key?

Yes. The registry entry asks for SUGRA_API_KEY.

Is Sugra API maintained?

The last commit was 4 days ago (2026-09-15). The latest release is v0.12.0.

Is Sugra API up?

100% of our last 1 checks got an answer. We check remote servers about four times a day.

What can I use instead of Sugra API?

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