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Fastbcp MCP

by arpe-ioversion 0.1.5i★ –iRuns on your computer
Registry nameio.github.arpe-io/fastbcp-mcp

Install

Overview

Description from the maker (original English)

MCP server for FastBCP — high-performance parallel database export to files and cloud

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Docs, sheets & office work

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: FASTBCP_PATH, FASTBCP_TIMEOUT, FASTBCP_LOG_DIR, LOG_LEVEL

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "fastbcp": {
      "command": "uvx",
      "args": [
        "fastbcp-mcp"
      ],
      "env": {
        "FASTBCP_PATH": "your-value",
        "FASTBCP_TIMEOUT": "your-value",
        "FASTBCP_LOG_DIR": "your-value",
        "LOG_LEVEL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add fastbcp --env FASTBCP_PATH=your-value --env FASTBCP_TIMEOUT=your-value --env FASTBCP_LOG_DIR=your-value --env LOG_LEVEL=your-value -- uvx fastbcp-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "fastbcp": {
      "command": "uvx",
      "args": [
        "fastbcp-mcp"
      ],
      "env": {
        "FASTBCP_PATH": "your-value",
        "FASTBCP_TIMEOUT": "your-value",
        "FASTBCP_LOG_DIR": "your-value",
        "LOG_LEVEL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "fastbcp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "fastbcp-mcp"
      ],
      "env": {
        "FASTBCP_PATH": "your-value",
        "FASTBCP_TIMEOUT": "your-value",
        "FASTBCP_LOG_DIR": "your-value",
        "LOG_LEVEL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • FASTBCP_PATH env
    Path to the FastBCP binary (optional — server runs in preview-only mode without it)
  • FASTBCP_TIMEOUT env
    Execution timeout in seconds (default: 1800)
  • FASTBCP_LOG_DIR env
    Directory for execution logs (default: ./logs)
  • LOG_LEVEL env
    Logging level: DEBUG, INFO, WARNING, ERROR (default: INFO)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account arpe-io, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Docs, sheets & office worki

  • More stars than 0% of the other 3547 servers in this category
  • Published to the registry more recently than 4% of the other 3547 servers in this category
  • Basic checks equal to or better than 100% of the other 3547 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Fastbcp MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.5) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs in the cloud

Excel MCP Server

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by haris-musaWidely used↓ 49.6K/wk★ 4.3KUpdated 7 days ago

Runs on your computer

MCP Server for Excel

Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

by sbroenneGrowing↓ 4.3K/wk★ 847Updated 5 days ago

Runs on your computer

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

FastBCP MCP Server

A Model Context Protocol (MCP) server that exposes FastBCP functionality for exporting data from databases to files (CSV, TSV, JSON, BSON, Parquet, XLSX, Binary) with optional cloud storage targets.

Overview

FastBCP is a high-performance CLI tool for exporting data from databases to files. This MCP server wraps FastBCP functionality and provides:

  • Safety-first approach: Preview commands before execution with user confirmation required
  • Password masking: Credentials and connection strings are never displayed in logs or output
  • Intelligent validation: Parameter validation with database-specific compatibility checks
  • Smart suggestions: Automatic parallelism method recommendations
  • Version detection: Automatic binary version detection with capability registry
  • Comprehensive logging: Full execution logs with timestamps and results

MCP Tools

1. preview_export_command

Build and preview a FastBCP export command WITHOUT executing it. Shows the exact command with passwords masked. Always use this first.

2. execute_export

Execute a previously previewed command. Requires confirmation: true as a safety mechanism.

3. validate_connection

Validate source database connection parameters (parameter check only, does not test actual connectivity).

4. list_supported_formats

List all supported source databases, output formats, and storage targets.

5. suggest_parallelism_method

Recommend the optimal parallelism method based on source database type and table characteristics.

6. get_version

Report the detected FastBCP binary version, supported types, and feature flags.

Installation

Prerequisites
  • Python 3.10 or higher
  • FastBCP binary v0.29+ (obtain from Arpe.io)
  • Claude Code or another MCP client
Setup
  1. Clone or download this repository:
   cd /path/to/fastbcp-mcp
  1. Install Python dependencies:
   pip install -r requirements.txt
  1. Configure environment:
   cp .env.example .env

Edit .env with your FastBCP path

  1. Add to Claude Code configuration (~/.claude.json):
   {

"mcpServers": { "fastbcp": { "type": "stdio", "command": "python", "args": ["/absolute/path/to/fastbcp-mcp/src/server.py"], "env": { "FASTBCP_PATH": "/absolute/path/to/FastBCP" } } } }

  1. Restart Claude Code to load the MCP server.
  1. Verify installation:
   # In Claude Code, run:

/mcp

You should see "fastbcp: connected"

Configuration

Environment Variables

Edit .env to configure:

# Path to FastBCP binary (required)

FASTBCP_PATH=./fastbcp/FastBCP

Execution timeout in seconds (default: 1800 = 30 minutes)

FASTBCP_TIMEOUT=1800

Log directory (default: ./logs)

FASTBCP_LOG_DIR=./logs

Log level (default: INFO)

LOG_LEVEL=INFO

Connection Options

The server supports multiple ways to authenticate and connect:

ParameterDescription
serverHost:port or host\instance (optional with connect_string or dsn)
user / passwordStandard credentials
trusted_authWindows trusted authentication
connect_stringFull connection string (excludes server/user/password/dsn)
dsnODBC DSN name (excludes server/provider)
providerOleDB provider name
application_intentSQL Server application intent (ReadOnly/ReadWrite)

Output Options

OptionCLI FlagDescription
format--formatOutput format: csv, tsv, json, bson, parquet, xlsx, binary
file_output--fileoutputOutput file path
directory--directoryOutput directory path
storage_target--storagetargetStorage: local, s3, s3compatible, azure_blob, azure_datalake, fabric_onelake
delimiter--delimiterField delimiter (CSV/TSV)
quotes--quotesQuote character
encoding--encodingOutput encoding
no_header--noheaderOmit header row (CSV/TSV)
decimal_separator--decimalseparatorDecimal separator (. or ,)
date_format--dateformatDate format string
bool_format--boolformatBoolean format: TrueFalse, OneZero, YesNo
parquet_compression--parquetcompressionParquet compression: None, Snappy, Gzip, Lz4, Lzo, Zstd
timestamped--timestampedAdd timestamp to output filename
merge--mergeMerge parallel output files

Export Options

OptionCLI FlagDescription
method--methodParallelism method
distribute_key_column--distributeKeyColumnColumn for data distribution
degree--degreeParallelism degree (default: 1)
load_mode--loadmodeAppend or Truncate
batch_size--batchsizeBatch size for export operations
map_method--mapmethodColumn mapping: Position or Name
run_id--runidRun ID for logging
data_driven_query--datadrivenqueryCustom SQL for DataDriven method
settings_file--settingsfileCustom settings JSON file
log_level--loglevelOverride log level (Information/Debug)
no_banner--nobannerSuppress banner output
license_path--licenseLicense file path or URL
cloud_profile--cloudprofileCloud storage profile name

Usage Examples

PostgreSQL to CSV Export
User: "Export the 'orders' table from PostgreSQL (localhost:5432, database: sales_db,

schema: public) to CSV file at /tmp/orders.csv. Use parallel export."

Claude Code will:

  1. Call suggest_parallelism_method to recommend Ctid for PostgreSQL
  2. Call preview_export_command with your parameters
  3. Show the command with masked passwords
  4. Explain what will happen
  5. Ask for confirmation
  6. Execute with execute_export when you approve
Export to Parquet with Compression
User: "Export the 'transactions' table from SQL Server to Parquet format

with Snappy compression, saved to /data/exports/."

Claude Code will use parquet format with parquet_compression set to Snappy.

Export to S3
User: "Export the 'users' table from PostgreSQL to CSV on S3 bucket

s3://my-bucket/exports/ using my AWS profile."

Claude Code will use storage_target=s3 with cloud_profile.

Check Version and Capabilities
User: "What version of FastBCP is installed?"

Claude Code will call get_version and display the detected version, supported source types, output formats, and available features.

Two-Step Safety Process

This server implements a mandatory two-step process:

  1. Preview - Always use preview_export_command first
  2. Execute - Use execute_export with confirmation: true

You cannot execute without previewing first and confirming.

Security

  • Passwords and connection strings are masked in all output and logs
  • Sensitive flags masked: --sourcepassword, --sourceconnectstring, -x, -g
  • Use environment variables for sensitive configuration
  • Review commands carefully before executing
  • Use minimum required database permissions

Testing

Run the test suite:

# Run all tests

python -m pytest tests/ -v

Run with coverage

python -m pytest tests/ --cov=src --cov-report=html

Project Structure

fastbcp-mcp/

src/ init.py server.py # MCP server (tool definitions, handlers) fastbcp.py # Command builder, executor, suggestions validators.py # Pydantic models, enums, validation version.py # Version detection and capabilities registry tests/ init.py test_command_builder.py test_validators.py test_version.py .env.example requirements.txt CHANGELOG.md README.md

License

This MCP server wrapper is provided as-is. FastBCP itself is a separate product from Arpe.io.

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