INDIVIDUAL MCP TOOL

dedupe_records

Remove duplicate JSON records using exact canonical matching or selected key fields.

humanmirror.frnone authenticationAvailability not checked

LIVE ENDPOINT

https://humanmirror.fr/api/forge/mcp

No auth detected

Connect to this endpoint to inspect the live schema for dedupe_records and invoke it with your own arguments.

{}

Potential side effects detected · medium confidence · heuristic, not a guarantee.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.humanmirror-forge]
url = "https://humanmirror.fr/api/forge/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "humanmirror-forge": {
      "type": "http",
      "url": "https://humanmirror.fr/api/forge/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: humanmirror-forge
Remote MCP URL: https://humanmirror.fr/api/forge/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "humanmirror-forge": {
      "url": "https://humanmirror.fr/api/forge/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "humanmirror-forge": {
      "type": "http",
      "url": "https://humanmirror.fr/api/forge/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "humanmirror-forge",
  "transport": "streamable-http",
  "url": "https://humanmirror.fr/api/forge/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

clean_json — Normalize JSON deterministically: trim strings, normalize object keys, optionally remove empty values, and sort keys for stable downstream processing.
normalize_entity — Normalize entity names, domains, URLs and emails into stable machine-readable canonical values.
score_data_quality — Score record-array data quality using completeness, duplicate rate and field type consistency.
detect_anomaly — Detect numeric outliers using IQR and robust summary statistics, returning machine-readable anomaly positions.