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faiaz000

fuzzy-match-mcp

by faiaz000
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
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.
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.
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.
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.
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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored 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

ActivityStale
ResponsivenessNo issues

Resources