How do I use fuzzy-match-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@fuzzy-match-mcp Find best matches for 'Samsung Galaxy S24' from my product list" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

fuzzy-match-mcp

by faiaz000

Overview Schema Source Related Servers Score Discussions Admin

Python

Local

Fuzzy Match MCP

A Python-based Model Context Protocol (MCP) server for deterministic fuzzy text matching.

It can normalize text, compare strings, rank possible matches, group duplicate values, and explain why two values match or differ. The server uses RapidFuzz for similarity scoring and can be used from MCP clients such as Cursor.

Features

Normalize text before comparison
Compare two strings using multiple similarity algorithms
Rank the best matches from a list
Detect and group probable duplicates
Explain matching results using token overlap
Use specialized matching profiles for companies, products, and addresses
Select different scoring strategies
Run locally through MCP over stdio
Testable with pytest

Related MCP server: EntityIdentification

Available MCP tools

`normalize_text`

Normalizes a text value before fuzzy matching.

Example input:

{
  "value": "  Müller & Söhne GmbH!  ",
  "profile": "general"
}

Example result:

{
  "original": "  Müller & Söhne GmbH!  ",
  "profile": "general",
  "normalized": "muller and sohne gmbh"
}

`compare_strings`

Compares two strings and returns detailed similarity scores.

Example input:

{
  "first": "Deutsche Bank AG",
  "second": "Deutsche Bank Aktiengesellschaft",
  "threshold": 90,
  "profile": "company",
  "strategy": "strict"
}

The response includes:

Normalized values
Individual similarity scores
Selected strategy
Final selected score
Match decision

`find_best_matches`

Ranks candidate strings according to their similarity with a query.

Example input:

{
  "query": "Samsung Galaxy S24",
  "choices": [
    "Apple iPhone 15",
    "Samsung Galaxy S24 128GB",
    "Galaxy S24 Smartphone",
    "Google Pixel 9"
  ],
  "limit": 3,
  "threshold": 40,
  "profile": "product",
  "strategy": "weighted"
}

`find_duplicate_groups`

Groups values that probably represent the same entity.

Example input:

{
  "values": [
    "Deutsche Bank AG",
    "Deutsche-Bank Aktiengesellschaft",
    "Deutsche Bank",
    "Commerzbank AG",
    "Commerz Bank",
    "Amazon Germany GmbH"
  ],
  "threshold": 80,
  "profile": "company",
  "strategy": "strict"
}

This is useful for:

Customer-data cleanup
Product-catalog deduplication
Company-name matching
Imported CSV cleanup
Contact-list deduplication

`explain_match`

Compares two values and explains the result using normalized tokens and score information.

Example input:

{
  "first": "Samsung Galaxy S24 128GB Black",
  "second": "Galaxy S24 Black 128 GB",
  "threshold": 75,
  "profile": "product",
  "strategy": "strict"
}

The response includes:

Common tokens
Tokens found only in the first value
Tokens found only in the second value
Token-overlap percentage
A deterministic explanation
Final match decision

Matching profiles

The server supports the following normalization profiles.

Profile	Description
`general`	Lowercases text, removes accents and punctuation, and collapses whitespace
`company`	Applies general normalization and removes common legal company suffixes
`product`	Standardizes product units and joins model names such as `S 24` → `s24`
`address`	Standardizes common address terms such as `street` → `st`

Company-profile example

Deutsche Bank AG
Deutsche Bank Aktiengesellschaft

Both normalize approximately to:

deutsche bank

Product-profile example

Samsung Galaxy S 24 128 GB

Normalizes to:

samsung galaxy s24 128gb

Matching strategies

Strategy	Description
`ratio`	Standard character similarity
`partial`	Finds the best matching substring
`token_sort`	Sorts tokens before comparison
`token_set`	Compares unique token sets
`weighted`	Uses RapidFuzz's weighted ratio
`strict`	Uses a composite score designed to reduce permissive partial matches

The strict strategy excludes partial_ratio from the final composite score because partial matching can be misleading when a short string appears inside a much longer string.

Thresholds

Similarity scores range from 0 to 100.

90–100: very strict
80–89: useful default for names and product titles
70–79: more permissive
Below 70: may create more false-positive matches

A result is considered a match when:

selected_score >= threshold

The ideal threshold depends on the dataset and the acceptable false-positive rate.

Project structure

fuzzy-match-mcp/
├── .cursor/
│   └── mcp.json
├── fuzzy_match_mcp/
│   ├── __init__.py
│   ├── grouping.py
│   ├── matching.py
│   ├── server.py
│   ├── tools.py
│   └── validators.py
├── tests/
│   ├── __init__.py
│   └── test_matching.py
├── main.py
├── pyproject.toml
├── README.md
└── uv.lock

Requirements

Python 3.10 or newer
uv
MCP Python SDK
RapidFuzz
pytest for development

Installation

Clone the repository:

git clone <your-repository-url>
cd fuzzy-match-mcp

Install the dependencies:

uv sync

If you are creating the project from scratch:

uv add "mcp[cli]" rapidfuzz
uv add --dev pytest

Running the tests

uv run pytest

Running with MCP Inspector

Use MCP Inspector during development:

uv run mcp dev main.py

This opens an MCP development interface where the registered tools can be inspected and called.

Running as a local stdio server

uv run python main.py

The process waits for MCP JSON-RPC messages through standard input.

Do not type into the terminal while the server is running over stdio. A blank terminal input is not a valid MCP JSON-RPC message.

Do not use ordinary print() statements in the server because standard output is reserved for MCP communication. Use logging through standard error instead.

Cursor configuration

Create:

.cursor/mcp.json

Use the following configuration on Windows:

{
  "mcpServers": {
    "fuzzy-match": {
      "type": "stdio",
      "command": "${workspaceFolder}/.venv/Scripts/python.exe",
      "args": [
        "${workspaceFolder}/main.py"
      ]
    }
  }
}

Then:

Open the repository root in Cursor.
Open Customize → MCPs.
Enable fuzzy-match.
Click Reload after changing the registered tools.
Use Cursor Agent to call the MCP tools.

Cursor starts the Python process automatically. Do not manually run main.py at the same time.

Example Cursor Agent prompts

Normalize text

Use the fuzzy-match normalize_text tool to normalize:

"  Müller & Söhne GmbH!  "

Use the general profile.

Compare company names

Use the fuzzy-match compare_strings tool.

Compare:
- Deutsche Bank AG
- Deutsche Bank Aktiengesellschaft

Use:
- profile: company
- strategy: strict
- threshold: 90

Rank product matches

Use the fuzzy-match find_best_matches tool.

Query:
Samsung Galaxy S24

Choices:
- Apple iPhone 15
- Samsung Galaxy S24 128GB
- Galaxy S24 Smartphone
- Google Pixel 9

Use the product profile and return the best three matches.

Group duplicates

Use the fuzzy-match find_duplicate_groups tool with:

- Deutsche Bank AG
- Deutsche-Bank Aktiengesellschaft
- Deutsche Bank
- Commerzbank AG
- Commerz Bank
- Amazon Germany GmbH

Use:
- profile: company
- strategy: strict
- threshold: 80

Explain a match

Use the fuzzy-match explain_match tool to compare:

- Samsung Galaxy S24 128GB Black
- Galaxy S24 Black 128 GB

Use:
- profile: product
- strategy: strict
- threshold: 75

How it works

The request flow is:

MCP client
    ↓
MCP tool in tools.py
    ↓
Input validation in validators.py
    ↓
Matching or grouping logic
    ↓
RapidFuzz
    ↓
Structured JSON result

Normalization happens before similarity scoring. It can include:

Unicode case folding
Accent removal
Punctuation replacement
Whitespace cleanup
Profile-specific transformations

Performance notes

find_best_matches compares one query against each candidate.

find_duplicate_groups performs pairwise comparisons. Its approximate comparison count is:

n × (n - 1) / 2

For this reason, duplicate grouping is intentionally limited to 500 values in the current version.

The grouping implementation uses union-find. If A matches B and B matches C, all three values can be placed in the same group even when A and C do not directly exceed the threshold.

Current limitations

Duplicate grouping is pairwise and is not intended for very large datasets.
Company suffixes and address aliases are rule-based and may not cover every country.
Product normalization supports only a small set of common units.
Fuzzy matching does not prove that two real-world entities are identical.
Thresholds should be evaluated against domain-specific examples before automatic merging.

Planned improvements

Structured record matching
Batch matching
Threshold evaluation
Custom normalization options
Additional international company suffixes
CSV import and export
Better canonical-value selection
More matching profiles
MCP resources and reusable prompts

Contributing

Contributions are welcome.

Suggested workflow:

git checkout -b feature/my-change
uv sync
uv run pytest

Before submitting a change:

Add tests for new behavior
Keep MCP tools focused
Avoid writing to standard output
Preserve structured JSON responses
Document new profiles and strategies

License

Add your chosen license before publishing the project.

A common choice for an open-source MCP server is the MIT License.

Available Tools

5 tools

compare_stringsC

    Compare two strings using fuzzy matching.

    Strategies:
    - ratio
    - partial
    - token_sort
    - token_set
    - weighted
    - strict

    Args:
        first: First text value.
        second: Second text value.
        threshold: Minimum score required for a match.
        profile: Normalization profile.
        strategy: Score-selection strategy.

ParametersJSON Schema

Name	Required	Default
`first`	Yes
`second`	Yes
`profile`	No	general
`strategy`	No	weighted
`threshold`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavior. It describes fuzzy matching and strategies but omits details like case sensitivity, how threshold is applied, normalization effects, or output shape. The output schema exists but is not referenced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and uses bullet points for strategies and args, which is clear. However, it redundantly lists arg names that are already in the schema, wasting space that could be used for additional guidance. A more concise and informative approach would be preferred.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no annotations, and an output schema, the description should cover return values and typical usage. It does not mention the output format (a score? a boolean?) or provide examples, leaving significant gaps for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must add meaning. It lists parameter names but provides no semantic details: e.g., what each 'profile' does, valid threshold range, strategy definitions. This barely adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Compare two strings using fuzzy matching' and lists specific strategies, making the tool's purpose explicit. It distinguishes itself from siblings like 'normalize_text' and 'find_best_matches' by focusing on pairwise comparison with multiple algorithms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs. alternatives (e.g., 'find_best_matches' or 'explain_match'). While strategies are listed, there is no explanation of which strategy suits what scenario, leaving the agent to guess.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

explain_matchB

    Compare two strings and explain why they match or differ.

    Args:
        first: First text value.
        second: Second text value.
        threshold: Minimum score required for a match.
        profile: Normalization profile.
        strategy: Score-selection strategy.

ParametersJSON Schema

Name	Required	Default
`first`	Yes
`second`	Yes
`profile`	No	general
`strategy`	No	strict
`threshold`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It only states basic functionality without disclosing side effects, computational cost, or that the tool is read-only. The description adds little beyond what is expected from the name.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with the main purpose stated first. The parameter list is slightly redundant given the schema, but it does not add unnecessary length. Overall, it is well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters (2 required, 2 enums) and no annotations, the description covers the basic operation but omits details about return value format (though output schema exists) and when to use specific profiles or strategies. It is minimally adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. However, it merely restates parameter names and types (e.g., 'First text value') without adding meaningful behavioral constraints or semantics, such as explaining the effect of different profiles or strategies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: compare two strings and explain why they match or differ. It distinguishes itself from siblings like compare_strings (which just compares) and find_best_matches (which finds best match rather than explaining a specific pair).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when an explanation of string matching is needed, but lacks explicit guidance on when to use this tool versus alternatives like compare_strings or find_duplicate_groups. No exclusions or context are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

find_best_matchesC

    Find and rank candidate strings most similar to a query.

    Args:
        query: Text to search for.
        choices: Candidate values.
        limit: Maximum number of matches.
        threshold: Minimum similarity score.
        profile: Normalization profile.
        strategy: Score-selection strategy.

ParametersJSON Schema

Name	Required	Default
`limit`	No
`query`	Yes
`choices`	Yes
`profile`	No	general
`strategy`	No	weighted
`threshold`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure, but it only lists parameters without explaining side effects, permissions, or output behavior beyond ranking. Critical traits like read-only or mutation status are absent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and structured as a docstring with a clear one-line summary. It lists parameters efficiently without extraneous text, though the parameter descriptions are too terse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, enums) and the presence of an output schema, the description omits crucial context such as how ranking works, how threshold and strategy interact, and when to use specific profiles. Sibling tools are not referenced for complementary use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description adds minimal meaning beyond parameter names (e.g., 'profile: Normalization profile' is vague). It does not explain enum options (e.g., what 'product' profile does) or the effect of strategy choices, failing to compensate for missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Find and rank candidate strings most similar to a query,' which is a specific verb+resource. It differentiates the tool from siblings like compare_strings (comparison) and normalize_text (normalization) by focusing on similarity ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like compare_strings or explain_match. There is no mention of prerequisites or exclusions, leaving the agent without context for appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

find_duplicate_groupsB

    Group strings that probably represent the same entity.

    Useful for:
    - company-name deduplication
    - customer-name cleanup
    - product catalogue cleanup
    - contact-list cleanup

    Args:
        values: Values to examine.
        threshold: Minimum score for joining a group.
        profile: Normalization profile.
        strategy: Score-selection strategy.

ParametersJSON Schema

Name	Required	Default
`values`	Yes
`profile`	No	general
`strategy`	No	strict
`threshold`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It describes the grouping action but lacks details on edge cases (e.g., empty list, duplicate values), performance considerations, or how scoring works internally. The brief parameter explanations do not cover behavioral nuances.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with a clear lead sentence, bullet-point use cases, and a labeled parameter list. It avoids unnecessary words and front-loads the core purpose, making it scannable for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, 2 enums, output schema present), the description covers purpose and basic parameter semantics. However, it omits expected output format (though output schema exists), behavioral constraints, and more detailed usage context relative to siblings, leaving room for improvement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides short explanations for each parameter (e.g., threshold: 'Minimum score for joining a group'), adding meaning beyond the schema's names and types. However, explanations are minimal and do not elaborate on how values affect behavior, leaving gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Group strings that probably represent the same entity,' which is a specific verb and resource. It lists concrete use cases like company-name deduplication and customer-name cleanup, distinguishing it from sibling tools like compare_strings or find_best_matches that focus on comparison or single match retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'Useful for' scenarios, giving context on when to use the tool. However, it does not discuss when not to use it or contrast with alternatives like compare_strings for pairwise comparison or explain_match for explanation, limiting guidance for an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

normalize_textA

    Normalize text before fuzzy matching.

    Profiles:
    - general: standard text normalization
    - company: removes legal company suffixes
    - product: standardizes product units and model names
    - address: standardizes common address terms

    Args:
        value: Text to normalize.
        profile: Type of normalization to apply.

ParametersJSON Schema

Name	Required	Description	Default
`value`	Yes
`profile`	No		general

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It briefly describes the effect of each profile (e.g., 'removes legal company suffixes') but does not disclose side effects, performance characteristics, or limitations. Minimal behavioral context but adequate for a simple normalization function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise: two sentences plus bullet points for profiles and an Args section. No unnecessary information. Front-loaded with the purpose, making it quick to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage context, parameters, and profiles. Since an output schema exists (not shown but noted), it does not need to explain return values. Lacks examples but is complete enough given the tool's simplicity and sibling context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It clearly defines both parameters: 'value' as text to normalize and 'profile' with enumerated options and their purposes. This adds significant meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it normalizes text before fuzzy matching, and lists four specific profiles (general, company, product, address) that distinguish its functionality. The name and description together make the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'before fuzzy matching', providing clear context for when to use this tool. While it does not explicitly exclude alternatives, the sibling tools (compare_strings, find_best_matches, etc.) are clearly about matching/comparing, so usage intent is well communicated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesv0.1.0Jul 28, 2026
- First observedcompare_strings
- First observedexplain_match
- First observedfind_best_matches
- First observedfind_duplicate_groups
- First observednormalize_text

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.6/5.0

Scored 2026-07-28 09:10 across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: direct comparison, normalization, candidate ranking, duplicate grouping, and detailed explanation. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (compare_strings, normalize_text, etc.) with clear and predictable naming.

Tool Count5/5

5 tools is well-scoped for a fuzzy matching server, covering essential operations without being excessive or sparse.

Completeness4/5

The surface covers normalization, comparison, matching, deduplication, and explanation. A minor gap is the lack of a tool to list available profiles/strategies, but descriptions provide that information.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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