# Scientific Microservices

> Use this tool when you need to perform reliable data engineering and statistical analysis, solving problems of data inconsistency and hallucinations in results. It provides valid outputs by processing input data through robust engineering and analysis protocols. Ideal for applications requiring trustworthy insights, such as scientific research and data-driven decision-making.

Canonical page: https://skillsregistry.net/skills/com-scientificmicroservices-mcp  
JSON: https://api.skillsregistry.net/v1/skills/com-scientificmicroservices-mcp

## Description

Valid and reliable data engineering and statistical analysis without hallucinations.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** data-analytics
- **Updated:** 2026-06-12

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/com.scientificmicroservices%2Fmcp)

## Use it

MCP endpoint published by the skill: `https://mcp.scientificmicroservices.com/mcp`

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "com-scientificmicroservices-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/com-scientificmicroservices-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/com-scientificmicroservices-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt