CivicDataForge Government Evidence vs Sugra API MCP
In-depth architectural comparison of the CivicDataForge Government Evidence and Sugra API MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
CivicDataForge Government Evidence
Data Platforms · Local stdio
Quality: 76/100 (Great) | Auth: API Key required
Sugra API MCP
Data Platforms · Local stdio
Quality: 60/100 (Good) | Auth: API Key required
Verdict Summary: Choose CivicDataForge Government Evidence if you need specialized Data Platforms tools running via a local process. Choose Sugra API MCP if your workspace requires Data Platforms integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose CivicDataForge Government Evidence when:
You need dedicated capabilities in the Data Platforms domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
You have access to required keys: APIFY_TOKEN.
Primary tools included: Ten source-bound government evidence tools, Unified evidence routing, Apify-backed execution with caller-owned credentials.
CivicDataForge Government Evidence is categorized under Data Platforms and uses a local stdio subprocess. In contrast, Sugra API MCP belongs to Data Platforms using local stdio subprocess. Select CivicDataForge Government Evidence when you need capabilities focused on data platforms and Sugra API MCP when you require tools for data platforms.
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.
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.
search_endpoints
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")
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. Call this after search_endpoints and before
call_endpoint when you need the exact parameter names and examples.
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.
list_toolsets
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.
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.
list_sources
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.
CivicDataForge is a production remote MCP server for AI agents that provides ten source-bound government-record evidence tools plus four run helpers across property compliance, healthcare exclusions, regulated facilities, environmental records, Norway company evidence, and jurisdiction routing. Public initialize and tool discovery require no credential; execution uses the caller’s own Apify API token in the X-Apify-Token header. Results preserve official source links, scope limits, provenance, and failure states through one stable endpoint.
Connector between LLM agents and world data - 1,500+ endpoints aggregating 160+ primary sources across 36 data domains: markets, macroeconomics, company fundamentals, government, news, climate, maritime, and entity screening. Install via pip install sugra-api-mcp or hosted at app.sugra.ai/mcp.