Awesome Agent Skills vs Sugra API
A side-by-side look at the Awesome Agent Skills and Sugra API MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Awesome Agent Skills for cloud platforms work as a local process; choose Sugra API for cloud platforms as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Awesome Agent Skills when
- You need dedicated capabilities in the Cloud Platforms domain.
- You prefer a local stdio subprocess transport.
- You want tools like
list_skills, get_skill, invoke_skill.
Choose Sugra API when
- You need dedicated capabilities in the Cloud Platforms domain.
- You prefer a local stdio subprocess transport.
- You want tools like
sugra_entity_screen, sugra_entity_lookup, search_endpoints.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Cloud Platforms | Cloud Platforms |
| Quality signal | 57/100 · Good | 59/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 26 | 3 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 4 | 8 |
| Full listing | View Awesome Agent Skills | View Sugra API |
Tools & capabilities
Awesome Agent Skills tools 4
list_skillsList all available agent skills that can be invokedget_skillGet detailed information and documentation for a specific skillinvoke_skillInvoke a skill with parameters to get formatted instructionsrefresh_skillsRefresh skills cache from the repository
Sugra API tools 8
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.fetch_dataOne-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_sourcesList 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.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Awesome Agent Skills
{
"mcpServers": {
"awesome-agent-skills": {
"command": "npx",
"args": [
"-y",
"awesome-agent-skills-mcp"
]
}
}
}
Sugra API
{
"mcpServers": {
"sugra-api": {
"command": "uvx",
"args": [
"sugra-api-mcp"
]
}
}
}
Frequently asked questions
Awesome Agent Skills is categorized under Cloud Platforms and uses a local stdio subprocess. Sugra API belongs to Cloud Platforms and uses a local stdio subprocess. Pick Awesome Agent Skills for cloud platforms work and Sugra API for cloud platforms.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Awesome Agent Skills has no declared pricing — check the project's own site. Sugra API has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add awesome-agent-skills and sugra-api as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Awesome Agent Skills has a quality signal of 57/100 (Good) with 1 views, 0 installs and 26 GitHub stars. Sugra API has 59/100 (Good) with 1 views, 0 installs and 3 GitHub stars.