mcp-entity-resolver

Make AI agents stop guessing entity names.

An MCP (Model Context Protocol) server that resolves messy company / customer / vendor names and IDs across CRM, ERP and ITSM systems so your agents act on the right record every time.

The Problem This Solves

AI agents routinely fail in enterprise environments because the same real-world entity appears as:

• "Acme Corp" in Salesforce

• "ACME CORPORATION" in SAP

• "Acme" in ServiceNow

• "acme-123" in a custom system

Without resolution, the agent updates the wrong record, creates duplicates, or refuses to act. This is one of the top reasons enterprise AI pilots die.

Related MCP server: Regex Toolkit MCP Server

Why This MCP?

• Computation-heavy, low external cost – pure fuzzy + rule-based matching (Levenshtein + normalization). Optional future embedding backend.

• Zero or near-zero API spend – works offline after entities are registered.

• Enterprise-ready tools – resolve, match lists, register confirmed entities, consistency checks.

• Designed for multi-system agents – Salesforce + SAP + ServiceNow + NetSuite in one conversation.

Tools

Quick Start

# Install
npm install -g mcp-entity-resolver   # or clone + npm i && npm run build

# Run (stdio – works with Claude Desktop, Cursor, etc.)
npx mcp-entity-resolver

Claude Desktop / Cursor config example

{
  "mcpServers": {
    "entity-resolver": {
      "command": "npx",
      "args": ["-y", "mcp-entity-resolver"]
    }
  }
}

Example Agent Conversation

User: "Update the billing address for Acme Corp in SAP"
Agent: (calls resolve_entity with name="Acme Corp")
→ gets canonical id from SAP with high confidence
→ proceeds safely

Pricing Suggestion (for marketplace)

• Free: up to 500 resolves / day

• Pro: $49/mo – unlimited + consistency reports

• Enterprise: $299/mo – private deployment + custom rules + audit log

Roadmap

• Optional local embedding model (all-MiniLM) for semantic boost

• Persistent store (SQLite / Postgres)

• Pre-built connectors that pull live from Salesforce / ServiceNow APIs

• Human-in-the-loop confirmation queue for low-confidence matches

Author

Prince Ruhul – Founder, Prevalid
GitHub: @princeruhulofficial

Built as part of the Daily AI Project Builder series.

License

MIT