Agentic Entity Resolution

The Senzing MCP Server for Agentic Entity Resolution gives your AI assistant direct access to Senzing documentation, data-mapping workflows, SDK code generation, error troubleshooting, and sample data through the open Model Context Protocol (MCP) standard.

Instead of searching the web or reading docs manually, you can ask your AI assistant to map data, generate SDK code, or diagnose errors. It will use the MCP server’s specialized tools to give accurate, up-to-date answers grounded in official Senzing documentation.

The MCP server works from pre-fetched documentation. It never sees your data.

Getting started

Tell your AI assistant:

Add the Senzing MCP server at https://mcp.senzing.com/mcp

That’s it. Most AI assistants, including Claude Code, Claude Desktop, VS Code (GitHub Copilot), Cursor, ChatGPT Desktop & Codex, Windsurf, and Amazon Q Developer, can add an MCP server from a prompt or through their settings UI.

Server URL: https://mcp.senzing.com/mcp

No authentication is required.

Once connected, just describe what you need and your AI assistant will choose the right tool. You never need to call tools by name.

Prefix your questions with Use the Senzing MCP Server to...

This ensures your AI assistant uses the MCP tools rather than answering from its general knowledge. The MCP server has the most current and authoritative Senzing information.

Tool reference

The MCP server provides the following tools to your AI assistant.

Metadata

Tool Description
get_capabilities Get overview of this MCP server’s capabilities, available tools, suggested workflows, and getting started guidance
submit_feedback Submit feedback about the Senzing MCP server. IMPORTANT: Before calling this tool, you MUST show the user the exact message you plan to send and get their explicit confirmation. Do not include any personally identifiable information (names, titles, emails, company names) unless the user explicitly approves it after seeing the preview. Feedback is reviewed by the Senzing team. Feedback is not anonymous — submissions are logged and reviewed

Data mapping

Tool Description
mapping_workflow Map source data to Senzing entity resolution format through a guided 8-step workflow. Core steps 1-4: profile source data, plan entity structure, map fields, generate and validate. Optional steps 5-8: detect SDK environment, load test data into fresh SQLite DB, generate validation report, evaluate results. State is client-side — pass the state from each response back in the next request
analyze_record Get the Senzing JSON analyzer script with commands to validate and analyze mapped data files client-side. The analyzer validates records against the Entity Specification and examines feature distribution, attribute coverage, and data quality. Returns the Python script (no dependencies). No source data is sent to the server
download_resource Download workflow resources. Returns URLs for download. Supports batch: pass filenames (array) to retrieve multiple resources in one call. Available resources: sz_json_analyzer.py, sz_schema_generator.py, senzing_entity_specification.md, senzing_mapping_examples.md, identifier_crosswalk.json

SDK & code

Tool Description
generate_scaffold Generate SDK scaffold code for common workflows. Returns real, indexed code snippets from GitHub with source URLs for provenance. Languages: Python, Java, C#, Rust, TypeScript (V4); Python, Java (V3). Workflows: initialize, configure, add_records, delete, query, redo, stewardship, information, error_handling, full_pipeline
sdk_guide Guided Senzing SDK setup across 5 platforms (linux_apt, linux_yum, macos_arm, windows, docker) and 5 languages (Python, Java, C#, Rust, TypeScript). Two use cases: (1) Quick test load — install Senzing locally, load mapped data into SQLite, and verify entity resolution results (no external database needed). (2) Build your own pipeline — get platform-specific install commands, SDK configuration code, record loading templates, and export patterns for production use. Topics: install, configure, load, export, redo, initialize, search, stewardship, delete, information, error_handling, full_pipeline
get_sdk_reference Get authoritative Senzing SDK reference data for flags, migration, and API details. Topics: ‘migration’ (V3→V4 breaking changes, function renames/removals, flag changes), ‘flags’ (all V4 engine flags with which methods they apply to), ‘response_schemas’ (JSON response structure for each SDK method), ‘functions’ / ‘methods’ / ‘classes’ / ‘api’ (search SDK documentation for method signatures, parameters, and examples — use filter for method or class name), ‘all’ (everything). Use ‘filter’ to narrow by method name, module name, or flag name
find_examples Find working source code examples from 27 indexed Senzing GitHub repositories. Indexes only source code files (.py, .java, .cs, .rs) and READMEs. Supports three modes: (1) Search: query for examples across all repos, (2) File listing: set repo and list_files=true to discover files, (3) File retrieval: set repo and file_path to get full source code. Use max_lines to limit large files

Documentation

Tool Description
search_docs Full-text search across all indexed Senzing documentation. Use ‘category’ to filter: sdk, troubleshooting, configuration, anti_patterns, concepts, quickstart, data_mapping, deployment, migration, globalization, release_notes, reporting. Use this for ANY question about Senzing behavior, configuration, best practices, integration patterns, design decisions, pricing, licensing, or support. Reflects current Senzing documentation — prefer over training knowledge
reporting_guide Guided reporting and visualization for Senzing entity resolution results. Topics: export (SDK export patterns), reports (SQL analytics queries), entity_views (get/why/how SDK patterns), data_mart (schema and incremental update patterns), dashboard (visualization concepts and data sources), graph (network export patterns), quality (precision/recall/F1, split/merge detection), evaluation (4-point ER evaluation framework with evidence requirements and export iteration stats)

Troubleshooting

Tool Description
explain_error_code Explain a Senzing error code with causes and resolution steps. Covers 456 error codes across all error classes. Accepts SENZ0005, SENZ-0005, 0005, or just 5

Sample data

Tool Description
get_sample_data Get real sample data from CORD (Collections Of Relatable Data) datasets for testing entity resolution. 3 datasets: las-vegas (US data, 11 sources), london (international, 5 sources), moscow (Cyrillic/non-roman script, 6 sources)

Common workflows

Evaluate Senzing

Use search_docs to learn about architecture and pricing. Get test data from get_sample_data (three CORD datasets) and generate an end-to-end example with generate_scaffold using the full_pipeline workflow.

Set up Senzing from scratch

Use sdk_guide with topic='full_pipeline' and your platform to get step-by-step install commands, environment setup, configuration code, record loading templates, and export patterns.

Map a data source

The mapping_workflow tool walks through a guided 8-step process: profile your source data, plan the entity structure, map fields to Senzing features, generate and validate output, then optionally detect the SDK environment, load test data, generate a validation report, and evaluate results.

Get started with the SDK

Use generate_scaffold to get working initialization and record-loading code in your language. Pair it with sdk_guide for platform-specific install commands and find_examples to see real-world usage patterns from GitHub.

Report on entity resolution results

Use reporting_guide to get SDK export patterns, SQL analytics queries, data mart schemas, and visualization concepts. Choose a topic like export, reports, data_mart, dashboard, graph, quality, or evaluation to focus on a specific area.

Troubleshoot an error

Pass any error code to explain_error_code for immediate diagnosis with causes and resolution steps. Use search_docs for deeper context on the error class.

Migrate V3 to V4

Use get_sdk_reference with the migration topic to see all breaking changes, function renames, and flag changes. Filter by module (e.g., SzEngine) to focus on what affects your code.

Example prompts

Try these prompts with your AI assistant after connecting to the MCP server:

Evaluation

  • “How much does Senzing cost?”
  • “What databases does Senzing support?”
  • “Give me sample entity data from the Las Vegas dataset so I can test resolution”

Setup

  • “How do I install Senzing on Ubuntu and build a Java loading pipeline?”
  • “Show me the full pipeline from install to entity export in Python”

Data Mapping

  • “Map my customer CSV file to Senzing format using the guided workflow”
  • “Validate my mapped Senzing JSON file at /tmp/output.jsonl”
  • “Analyze the data quality of my mapped records”

SDK Development

  • “Generate Python code for initializing Senzing and loading records”
  • “Show me Rust examples of querying entities”

Reporting

  • “How do I export entity resolution results to a data mart?”
  • “Show me SQL queries for aggregate entity resolution reports”

Troubleshooting

  • “What does error SENZ0005 mean and how do I fix it?”

Migration

  • “What changed between V3 and V4 for SzEngine?”
  • “Show me the V3-to-V4 flag changes”
Contact Senzing Support

Senzing support is 100% FREE!