MCP Server for Entity Resolution and Data Matching

Get a Demo Start Free Trial

The MatchLogic MCP Server lets AI agents match, deduplicate, and resolve entity records through the Model Context Protocol, with all matching running inside your own environment. Any MCP-compatible agent, including Claude, ChatGPT, and Microsoft Copilot Studio, can call MatchLogic's matching engine as a tool: checking whether two records are the same supplier, deduplicating a contact list before a campaign, or resolving an account against master data before acting on it.

Book a Demo Start Free Trial

Resolve from evidence, not assumptions

Most resolution errors trace back to bad inputs. Before the AI resolves anything, the MatchSense profiler shows you exactly what every column contains, how complete it is, and whether it is reliable enough to feed into resolution.

Completeness and Null Analysis

Every column shows its fill rate at a glance. A 95% populated email is worth resolving on; an identifier filled half the time is not, and you know which is which before you start.

Semantic Classification

The profiler labels each column by what it actually is: a name, an address, an identifier, a date, a currency value, and so on. You can see immediately which columns line up across sources.

Entropy and Anomaly Detection

Entropy measures how varied a column is. Unusually low variation on a name field points to a data problem, while anomaly checks catch the outliers that would otherwise distort results.

Word Frequency Analysis through Vocabulary Governance

Vocabulary Governance counts how often each value occurs. Point it at a company-name column and every form of “Inc”, “Corp”, “Holdings”, and “Company” appears with its frequency, turning a days-long cleanup into a short one.

Rated faster and more accurate than IBM and SAS.

Speed and accuracy are not marketing claims. They are the results of independent benchmark studies conducted across 15 product comparisons with university, government, and private-sector datasets ranging from 80,000 to 8 million records.

96%

Average match accuracy across datasets

10%+

More true matches found vs. competing commercial tools

Fewest

False positives across all independent benchmark studies

In-memory processing at enterprise scale

MatchSense processes millions of records in memory. You load your data, run the pipeline, review the results, adjust, and re-run without writing to disk between steps. This is how a single analyst can deduplicate an 8-million-record vendor master in an afternoon instead of a week.

Proprietary algorithms refined over 19 years

What is an MCP server for data matching?

An MCP server for data matching exposes a matching engine's capabilities, such as fuzzy matching, deduplication, and entity resolution, as standardized tools that AI agents can discover and call.A golden record is the single best version of an entity, drawn from the strongest fields across every system. The AI decides which records describe the same entity; survivorship rules you control decide which of their values to keep.

MCP (Model Context Protocol) is an open standard introduced by Anthropic in November 2024 and since adopted across major AI platforms, including OpenAI and Microsoft.You write the field-level rules: trust this source over that one, prefer the most complete value, favor the most recent, or apply your own logic. The rules then run across every resolved entity without manual work.

Instead of building a separate integration for every agent framework, you connect MatchLogic once over MCP, and every compatible agent, copilot, and workflow can use the same matching tools with the same permissions and the same audit trail.

Why do AI agents need entity resolution?

AI agents fail on duplicate and fragmented data in a way traditional software does not: they act on it. An agent that sees "Acme Corp", "ACME Corporation", and "Acme Inc." as three different companies will email the same customer three times, approve a supplier that was blocked under a different spelling, or report pipeline numbers that double count accounts. Poor data quality already costs organizations an average of $12.9 million per year, according to Gartner, and agents compound the problem by making decisions at machine speed and at scale.An MCP server for data matching exposes a matching engine's capabilities, such as fuzzy matching, deduplication, and entity resolution, as standardized tools that AI agents can discover and call.A golden record is the single best version of an entity, drawn from the strongest fields across every system. The AI decides which records describe the same entity; survivorship rules you control decide which of their values to keep.

Entity resolution is the trust layer that fixes this. It gives every agent one accurate answer to the question that underpins almost every business action: which of these records refer to the same real-world supplier, customer, contact, or product. With the MatchLogic MCP Server, agents get that answer from a governed matching engine instead of guessing from raw CRM or ERP data.

What can AI agents do with the MatchLogic MCP Server?

The MCP server exposes MatchLogic’s core matching capabilities as callable tools. In practice, connected agents can:

Compare records on demand.

An agent passes two or more records and receives a match decision with a confidence score and a plain-language explanation of which fields matched and how. Useful inside approval flows, onboarding checks, and support workflows.

Deduplicate datasets.

An agent points MatchLogic at a table, file, or query result and receives duplicate groups ranked by confidence. Useful before campaign sends, data migrations, and reporting runs.

Resolve an entity against master data.

An agent submits a single record, such as a new vendor from an invoice, and receives the best existing match from your supplier, customer, contact, or product data, or confirmation that no match exists. Useful for preventing duplicate creation at the point of entry.

Search matched data with natural language.

An agent can ask questions like "do we already have this supplier under another name" and get an answer grounded in MatchLogic's fuzzy matching rather than exact string search.

How does it work?

Deploy the MCP server alongside MatchLogic.

It runs on the same on-premise infrastructure as your existing MatchLogic installation. There is no separate cloud service to stand up.

Register the endpoint with your agent platform.

Add the MatchLogic MCP Server as a connector in Claude, ChatGPT, Copilot Studio, or any custom MCP client, with the credentials and permissions you choose.

Agents discover and call the tools.

Connected agents see the available matching tools automatically and call them as part of their workflows, passing records or dataset references.

MatchLogic runs the match and returns governed results.

The same fuzzy and exact matching engine you already trust processes the request locally and returns decisions, confidence scores, and explanations, all captured in the audit log.

Watch MatchSense resolve your data in minutes

Every demo starts with your data. Bring a sample file and we will walk through profiling, cleansing, AI resolution, and golden record assembly live. You will see how the engine handles your specific identity challenges, with no slide decks and no hypothetical scenarios.

Schedule a Demo