Senzing Entity Resolution — MCP Skill

Use this skill whenever a task involves entity resolution, record linkage, deduplication, or any interaction with the Senzing platform.

What Is Senzing

Senzing provides real-time AI-powered entity resolution as an embeddable SDK. It determines when two records refer to the same real-world entity (person, organization, etc.) by analyzing names, addresses, identifiers, and other attributes across data sources — without training data or manual rules.

MCP Server Setup

The Senzing MCP server is a remote server. Connect it to your client:

Claude Code:

claude mcp add --transport http senzing https://mcp.senzing.com/mcp

Claude Desktop / Other MCP Clients — add to your MCP config:

{
  "mcpServers": {
    "senzing": {
      "type": "url",
      "url": "https://mcp.senzing.com/mcp"
    }
  }
}

The server works from pre-fetched documentation — it never connects to live Senzing instances and never handles PII. It also hosts official Senzing SDK .deb packages at /downloads/ for direct download in firewalled environments — sdk_guide returns download URLs and install commands automatically.

Tool Reference

Start any Senzing session by calling get_capabilities for an up-to-date tool listing and suggested workflows.

Data Mapping (4 tools)

Documentation & Reference (3 tools)

SDK Setup & Code Generation (2 tools)

Sample Data (1 tool)

Reporting & Visualization (1 tool)

Troubleshooting (1 tool)

Meta & Utility (2 tools)

Key Workflows

1. Map Source Data to Senzing Format

This is the most common workflow. Follow these steps:

1. Call mapping_workflow with action='start' and the source file paths.
2. Walk through each step: Profile → Plan → Map → Codegen → QA.
3. At each step, pass the state object from the previous response.
4. After codegen, use lint_record to validate the output JSON.
5. Use analyze_record to check feature distribution and coverage.
6. If the linter or analyzer scripts fail to download, use download_resource.

Tips:

• The workflow generates a mapper script — run it locally to produce JSONL.
• Profile step: read the source data yourself or run the profiler script.
• Plan step: identify master entities vs. child records vs. relationships.
• Map step: map every source field to a Senzing feature with a confidence score.
• QA step: evaluate whether the output meets quality thresholds.

2. Set Up the Senzing SDK

1. Call sdk_guide with topic='install' — it returns a platform decision tree.
2. Call again with the chosen platform (e.g., topic='install', platform='linux_apt').

• For firewalled environments, the response includes direct_download with

.deb package URLs from mcp.senzing.com/downloads/ — no apt repo needed.

1. Call sdk_guide with topic='configure' for engine configuration code.
2. Call sdk_guide with topic='load' for record loading code.
3. Or use topic='full_pipeline' for install + configure + load + export in one call.

3. Generate SDK Integration Code

1. Call generate_scaffold with the target language and workflow.

• Start with workflow='initialize' for engine setup.
• Then workflow='add_records' for record loading.
• Use workflow='full_pipeline' for an end-to-end example.

1. Call find_examples to find real-world usage patterns.
2. Use search_docs for API details and deployment guidance.

4. Troubleshoot Errors

1. Call explain_error_code with the code from the user's logs.
2. Follow the resolution steps in the response.
3. Call search_docs for additional context on the error class.

5. Evaluate Senzing

1. search_docs — learn about architecture (embedded SDK, air-gapped deployment).
2. search_docs with query "pricing" — DSR pricing model.
3. get_sample_data — get real test data from CORD datasets.
4. generate_scaffold with workflow='full_pipeline' — end-to-end example.

6. Migrate V3 to V4

1. get_sdk_reference with topic='migration' — all breaking changes.
2. Filter by module: topic='migration', filter='SzEngine'.
3. get_sdk_reference with topic='flags' — new flag system (SZ_WITH_INFO replaces WithInfo functions).

7. Build ER Reporting

1. Call reporting_guide with topic='export' and your language to get data extraction code.
2. Call reporting_guide with topic='reports' to get SQL for the 4 core aggregate reports.
3. Call reporting_guide with topic='data_mart' to get the analytical schema and incremental update patterns.
4. Call reporting_guide with topic='dashboard' for visualization concepts and chart data sources.
5. Call reporting_guide with topic='graph' for network graph export patterns.

8. Check for Common Pitfalls

Before recommending installation, architecture, or deployment approaches:

1. Call search_docs with category='anti_patterns' and a query describing what you plan to recommend.
2. Review any matching anti-patterns before proceeding.
3. Note: sdk_guide also returns topic-specific anti-patterns inline.

9. Deploy Senzing

1. search_docs with your platform (e.g., "docker quickstart", "AWS deployment").
2. search_docs for database setup (PostgreSQL, MySQL, MSSQL).
3. search_docs for engine configuration and tuning guidance.
4. generate_scaffold with workflow='initialize' for your language.

Critical Rules

These rules are non-negotiable. Violating them produces incorrect output.

1. Never hand-code Senzing JSON — use mapping_workflow. Training data produces

wrong attribute names (e.g., BUSINESS_NAME vs correct NAME_ORG).

1. Never guess SDK methods — use generate_scaffold or get_sdk_reference.

Methods changed between V3 and V4.

1. Check anti-patterns first — before recommending installation or deployment:

search_docs(query="topic", category="anti_patterns").

1. MCP first for all Senzing questions — search_docs covers pricing,

architecture, deployment, SDK, database tuning, globalization, and more. It reflects current releases — prefer it over training knowledge.

1. Discover tools dynamically — call get_capabilities rather than assuming

tool names from this file or training data.

Best Practices

• Always call `get_capabilities` first to get current tool count and workflows.
• Prefer `search_docs` over web search for any Senzing-related question.

The MCP server indexes authoritative content that may not rank well on the web.

• Pass state faithfully in mapping_workflow — the server is stateless,

all workflow state lives in the client.

• Never send source data to the server. The lint_record and analyze_record

tools return scripts that run locally. The mapping workflow sends field names and schema — not row-level data.

• Present `download_url` from get_sample_data results directly to the user.

Do not dump raw CORD records into the conversation — they are a preview only.

• Version parameter: Most tools accept version. Use "current" for the

latest Senzing version unless the user specifies V3 (use "3.x").

Entity Resolution Concepts

When discussing Senzing with users, these terms are important:

• Entity — A real-world person, organization, or object represented by one

or more records across data sources.

• Feature — An attribute used for matching: NAME, ADDRESS, PHONE, DOB,

SSN, PASSPORT, etc. Senzing supports 100+ features across 30+ feature types.

• Data Source — A labeled origin for records (e.g., "CUSTOMERS", "WATCHLIST").

Every record must have a DATA_SOURCE and RECORD_ID.

• Entity Type — PERSON or ORGANIZATION (default: PERSON).
• Matched — Records confirmed as the same entity.
• Possible Match — Records that might be the same entity but need review.
• Relationship — A declared or discovered connection between entities.
• DSR (Disclosed, Sized, Resolved) — Senzing's pricing unit. One DSR equals

one record loaded into the engine.

Examples

Example 1: Map a CSV file

User: "I have a customer CSV at /data/customers.csv I need to load into Senzing"
→ Call mapping_workflow(action='start', file_paths=['/data/customers.csv'])
→ Walk through all 5 steps, passing state each time
→ Run lint_record on the output JSONL
→ Run analyze_record to check quality

Example 2: Set up Senzing SDK on Linux

User: "Help me install and set up the Senzing SDK on Ubuntu"
→ Call sdk_guide(topic='install', platform='linux_apt', version='current')
→ Present install commands and engine config
→ If user has firewall issues, use the direct_download URLs from the response
→ Call sdk_guide(topic='configure', platform='linux_apt', language='python', version='current')
→ Present configuration code

Example 3: Generate Python loader code

User: "Write me Python code to initialize Senzing and load records"
→ Call generate_scaffold(language='python', version='current', workflow='initialize')
→ Call generate_scaffold(language='python', version='current', workflow='add_records')
→ Combine and present the code

Example 4: Debug an error

User: "I'm getting SENZ7234 when loading records"
→ Call explain_error_code(error_code='7234', version='current')
→ Present causes and resolution steps
→ Use search_docs if additional context is needed