Senzing Entity Resolution Skill

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 (3 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', the source file paths, and a

writable workspace_dir. **A readable file is not
required.** file_paths is a label the server records — it never opens the
file — so if the user has only described their columns, start anyway using
whatever name they used. Never hand-code a mapping because you cannot read
the file.

1. Walk through steps 1–4 (core): Profile → Plan → Map → Generate & Validate.

At step 1 you may run the profiler script, read the data yourself, or
write the profile_summary from the user's own description of their columns
when there is no readable file and no shell.

1. At each step, pass the state object from the previous response.
2. Use analyze_record to check feature distribution, coverage, and Entity

Specification compliance on the output JSON.

1. Optionally continue steps 5–8 to sandbox-load the mapped data into a fresh

SQLite DB — this catches mapping issues the static analyzer misses. This is
mapping validation only, not production loading.

1. If the 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.
• Generate & Validate step: emit sample JSON, analyze it, then write and run the

mapper before rendering a verdict.

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 (CORD datasets or the pre-mapped truthset).
4. generate_scaffold with workflow='full_pipeline' — end-to-end example.
5. submit_feedback with category='license_request' — request a free 10-day,

250K-record evaluation license (preview details with the user first).

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).
4. get_sdk_reference with topic='parameters', filter=<method>, language=<yours> — the V4 argument types for each call you are porting.

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.
6. Call reporting_guide with topic='quality' for precision/recall, split/merge detection, and review queue strategies.
7. Call reporting_guide with topic='evaluation' for the 4-point ER evaluation framework with evidence requirements.

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 or argument types — use generate_scaffold or

get_sdk_reference. Methods changed between V3 and V4, and the same method
has a different name and different argument types in each binding:
Python find_network_by_entity_id(entity_ids: List[int], …), Java
findNetwork(SzEntityIds, …), C# FindNetwork(ISet<long>, …), Rust
find_network_by_entity_id(&[EntityId], …), TypeScript
findNetwork(number[], …). **Never translate a call from one binding to
another** — call `get_sdk_reference(topic='parameters', filter=<method>,
language=<yours>)` and use the returned signature verbatim.

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 analyze_record tool returns a

script that runs 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: All tools accept version and default to "current"

(latest V4). Most tools return current/V4 content regardless of the token
passed — for legacy V3 work, rely on generate_scaffold (Python V3 is a
distinct variant) and get_sdk_reference(topic='migration').

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 — A category of matchable identity data: NAME, ADDRESS, PHONE,

DOB, SSN, PASSPORT, etc. Senzing supports 30+ features.

• Attribute — A specific mappable field within a feature (e.g., NAME_ORG and

NAME_FULL under NAME; ADDR_LINE1 under ADDRESS). 100+ attributes across the
30+ features.

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

Every record must have a DATA_SOURCE and RECORD_ID.

• RECORD_TYPE — Optional (Recommended) feature that prevents records of

different types from resolving together. Standard values: PERSON, ORGANIZATION
(watchlists also commonly use VESSEL, AIRCRAFT). Include when known; leave
blank if unknown — there is no default.

• 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 (Data Source Record) — Senzing's subscription pricing unit: one record

mapped and loaded into Senzing, keyed by DATA_SOURCE code + RECORD_ID. Updates
and searches are not counted; deletes generally reduce the count.

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'], workspace_dir='/data/senzing-work')
→ Walk through core steps 1–4, passing state each time
→ Run analyze_record to check quality and Entity Specification compliance
→ Optionally continue steps 5–8 to sandbox-load into SQLite and validate the mapping

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