mcpbeat

Sugra API MCP Server

ai.sugra/api-mcp
answering

Sugra API is answering right now. Last checked 10 min ago. 317 installs a week from pypi. It exposes 11 tools. Last commit 31 Jul 2026.

Gateway between LLM agents and world data through eight tools and a bundled endpoint catalog.

Installs per day peak 998 · avg 88 · -11% w/w
a month agotoday
Uptime history 41 hours of history
41 hours agonow
100.0%
Uptime 24h
91 of 91 checks
11
Tools
read from the server
376 ms
Response time
average over 24h
317
Installs / week
npm and PyPI

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 10 min ago.

run in your terminal
claude mcp add api-mcp --transport http https://app.sugra.ai/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "api-mcp": {
      "url": "https://app.sugra.ai/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.api-mcp]
url = "https://app.sugra.ai/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "api-mcp": {
      "url": "https://app.sugra.ai/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "api-mcp": {
      "url": "https://app.sugra.ai/mcp"
    }
  }
}

Available tools 11

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

sugra
sugra_entity_lookup
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_screen
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.
call
call_endpoint
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.
data
fetch_data
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.
describe
describe_endpoint
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.
endpoints
search_endpoints
Search the bundled Sugra endpoint catalog by natural-language query.
resolve
resolve_entity
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. 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.
snapshot
get_snapshot
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": ...}).
sources
list_sources
List endpoint source families derived from catalog metadata.
timeseries
get_timeseries
Bounded timeseries for an entity: price, macro_series, or etf_flows. Returns points oldest-first with an explicit downsampling flag when the raw series exceeded max_points. etf_flows is filing-cadence (one point per SEC filing refresh), NOT per calendar day, so even a wide window yields a handful of points. Times are UTC. Costs 1 unit per call. Args: metric: One of price / macro_series / etf_flows. entity: Entity dict from resolve_entity ({"namespace": ..., "ids": ...}). granularity: Requested point granularity (default "1d"). max_points: Hard cap on returned points (default 500).
toolsets
list_toolsets
List endpoint groups available in the bundled catalog.

Endpoints

URLTransportStateLatencyChecked
https://app.sugra.ai/mcp streamable-http answering 390 ms 10 min ago

Sugra API — questions

Answers built from our own checks of this server.

What can Sugra API do?
It exposes 11 tools, read directly from the server on our last check. Among them: call_endpoint, describe_endpoint, fetch_data, get_snapshot, get_timeseries, list_sources and 5 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Sugra API working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 376 ms. The bar chart above shows every period we have measured.
How do I connect Sugra API?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Sugra API need an API key?
No. Sugra API completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 11 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Sugra API?
It answers our handshake in 376 ms on average, which is faster than 37% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use Sugra API?
The pypi package sugra-api-mcp was installed 317 times in the last week. Week over week that is -11%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is Sugra API open source?
Yes — it is published under the MIT licence, written in Python, 2 stars on GitHub and 3 open issues. The source link is on this page, so you can read exactly what it does with your data before you connect it.