cdata-mcp-sheets-read-only-gateway

Overview of CData's Restricted MCP Endpoint for Google Sheets Data

:warning: ATTENTION: This specific implementation functions exclusively in read-only mode. For comprehensive data manipulation capabilities (Read, Write, Update, Delete, and invoking Actions) coupled with an expedited setup process, please consult the complimentary, feature-rich CData MCP Server for Google Sheets (Beta release).

Core Objective

This read-only MCP service was engineered to empower Large Language Models (LLMs), such as Claude Desktop, with the capability to query live datasets residing within Google Sheets. Access is mediated through the robust CData JDBC Driver for Google Sheets.

The CData JDBC Driver translates Google Sheets structures into conventional relational SQL schemas. This server component encapsulates that driver, exposing the Sheet data via a streamlined MCP interface, thereby enabling LLMs to fetch current information via natural language inquiries—SQL proficiency is not necessary.

Implementation & Configuration Instructions

1. Acquire the Source Code: Clone the repository: bash git clone https://github.com/cdatasoftware/google-sheets-mcp-server-by-cdata.git cd google-sheets-mcp-server-by-cdata

2. Compile the Application: Execute the build command: bash mvn clean install

   This operation generates the executable JAR: CDataMCP-jar-with-dependencies.jar 3. Obtain and Install the CData JDBC Driver: Download the necessary driver package from: https://www.cdata.com/drivers/gsheets/download/jdbc 4. Driver Authorization: Activate the CData JDBC Driver: * Navigate to the lib subdirectory within the driver's installation path (e.g., Windows: C:\Program Files\CData\CData JDBC Driver for Google Sheets\ or Linux/Mac: /Applications/CData JDBC Driver for Google Sheets/). * Execute the licensing utility: java -jar cdata.jdbc.googlesheets.jar --license * Input your registration details and use "TRIAL" or your valid license key. 5. Define Data Source Connection: Establish parameters for the underlying data connection: * Invoke the Connection String utility: java -jar cdata.jdbc.googlesheets.jar * Configure the necessary string (e.g., authentication details for OAuth). Verify connectivity using "Test Connection". * Securely copy the resulting connection string. 6. Create the Configuration Properties File (.prp): Generate a properties file (e.g., google-sheets.prp) containing the following critical configuration parameters: * Prefix: A short identifier for the exposed tools. * ServerName: The operational name assigned to this server instance. * ServerVersion: Version marker for the service. * DriverPath: Absolute file system path to the downloaded JDBC JAR. * DriverClass: The fully qualified name of the JDBC driver implementation class (e.g., cdata.jdbc.googlesheets.GoogleSheetsDriver). * JdbcUrl: The connection string obtained in the previous step. * Tables: Specify desired tables; leave empty (Tables=) to grant access to all available resources. env Prefix=googlesheets ServerName=CDataGoogleSheets ServerVersion=1.0 DriverPath=PATH\TO\cdata.jdbc.googlesheets.jar DriverClass=cdata.jdbc.googlesheets.GoogleSheetsDriver JdbcUrl=jdbc:googlesheets:InitiateOAuth=GETANDREFRESH; Tables=

Integrating with the Client Application (e.g., Claude Desktop)

1. Client Configuration Update: Generate or modify the client's configuration manifest (e.g., claude_desktop_config.json) to incorporate the new MCP endpoint definition within the mcpServers object.

   Windows Example:

   { "mcpServers": { "{classname_dash}": { "command": "PATH\TO\java.exe", "args": [ "-jar", "PATH\TO\CDataMCP-jar-with-dependencies.jar", "PATH\TO\google-sheets.prp" ] }, ... } }

   Linux/Mac Example:

   { "mcpServers": { "{classname_dash}": { "command": "/PATH/TO/java", "args": [ "-jar", "/PATH/TO/CDataMCP-jar-with-dependencies.jar", "/PATH/TO/google-sheets.prp" ] }, ... } }

   If necessary, relocate the finalized configuration file to the client's designated profile directory: Windows Relocation: bash cp C:\PATH\TO\claude_desktop_config.json %APPDATA%\Claude\claude_desktop_config.json

   Linux/Mac Relocation: bash cp /PATH/TO/claude_desktop_config.json /Users/{user}/Library/Application\ Support/Claude/claude_desktop_config.json'

2. Client Refresh: Restart or reload the client application entirely to ensure the newly defined MCP Server is recognized.

Note: A complete termination and restart of the client application (e.g., using Task Manager/Activity Monitor) is often required for new MCP Servers to become visible.

Standalone Server Execution

To operate the server independently (without direct integration into a client environment that uses stdio): bash java -jar /PATH/TO/CDataMCP-jar-with-dependencies.jar /PATH/TO/Salesforce.prp

Critical Note: This server exclusively communicates via standard input/output (stdio), necessitating that the consuming client application execute on the same host machine.

Operational Guidance

Once the MCP service is successfully configured, the integrated AI client gains access to perform data inquiries against the backend source. Explicit tool invocation is usually unnecessary; simply pose direct questions about the data you wish to analyze. Examples: * "Identify the relationship between successfully closed sales and the client's primary industry sector." * "Tally the count of pending service requests within the designated SUPPORT queue." * "List all scheduled appointments associated with today's date."

Available Tools and Functionality Descriptions

Tool identifiers below utilize {servername} as a placeholder for the name defined in your configuration (e.g., googlesheets).

• {servername}_get_tables: Fetches a comprehensive manifest of accessible tables within the connected data repository. For schema detail, chain this with the {servername}_get_columns tool. Output is delivered in CSV format, with the initial row serving as column headers.
• {servername}_get_columns: Retrieves the structural definition (columns) for a specified table. Always precede this with {servername}_get_tables to identify valid targets. Output format is CSV, including column names as the first line.
• {servername}_run_query: Permits the execution of arbitrary SQL SELECT statements against the exposed data model.

JSON-RPC Interaction Examples (For Scripted Access)

If bypassing the AI client interface for direct interaction (adhering to JSON-RPC 2.0 standards), refer to these request body templates when invoking the defined tools:

Example: Requesting Table List (google_sheets_get_tables)

{ "jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": { "name": "google_sheets_get_tables", "arguments": {} } }

Example: Requesting Schema Details (google_sheets_get_columns)

{ "jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": { "name": "google_sheets_get_columns", "arguments": { "table": "Account" } } }

Example: Executing a Data Retrieval Query (google_sheets_run_query)

{ "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "google_sheets_run_query", "arguments": { "sql": "SELECT * FROM [Account] WHERE [IsDeleted] = true" } } }

Common Issue Resolution

1. Server Visibility Failure: If the MCP Server does not appear in the client GUI, confirm that the client application has been completely shut down (Task Manager/Activity Monitor check) and relaunched.
2. Data Retrieval Errors: Verify that the connection parameters in your .prp file accurately reflect the output from the Connection String utility.
3. Connectivity Problems: For issues directly related to connecting to Google Sheets, please engage the CData Support Team.
4. MCP Server Feedback: For suggestions or operational issues with the server itself, participate in the CData Community.

Licensing

This MCP server software is distributed under the permissive MIT License. Details concerning usage, modification, and distribution rights are fully specified in the accompanying LICENSE file.

Extensive Data Source Compatibility List (Via Underlying Driver)

Source	Source	Source	Source
Access	Act CRM	Act-On	Active Directory
ActiveCampaign	Acumatica	Adobe Analytics	Adobe Commerce
ADP	Airtable	AlloyDB	Amazon Athena
Amazon DynamoDB	Amazon Marketplace	Amazon S3	Asana
Authorize.Net	Avalara AvaTax	Avro	Azure Active Directory
Azure Analysis Services	Azure Data Catalog	Azure Data Lake Storage	Azure DevOps
Azure Synapse	Azure Table	Basecamp	BigCommerce
BigQuery	Bing Ads	Bing Search	Bitbucket
Blackbaud FE NXT	Box	Bullhorn CRM	Cassandra
Certinia	Cloudant	CockroachDB	Confluence
Cosmos DB	Couchbase	CouchDB	CSV
Cvent	Databricks	DB2	DocuSign
Dropbox	Dynamics 365	Dynamics 365 Business Central	Dynamics CRM
Dynamics GP	Dynamics NAV	eBay	eBay Analytics
Elasticsearch	Email	EnterpriseDB	Epicor Kinetic
Exact Online	Excel	Excel Online	Facebook
Facebook Ads	FHIR	Freshdesk	FTP
GitHub	Gmail	Google Ad Manager	Google Ads
Google Analytics	Google Calendar	Google Campaign Manager 360	Google Cloud Storage
Google Contacts	Google Data Catalog	Google Directory	Google Drive
Google Search	Google Sheets	Google Spanner	GraphQL
Greenhouse	Greenplum	HarperDB	HBase
HCL Domino	HDFS	Highrise	Hive
HubDB	HubSpot	IBM Cloud Data Engine	IBM Cloud Object Storage
IBM Informix	Impala	Instagram	JDBC-ODBC Bridge
Jira	Jira Assets	Jira Service Management	JSON
Kafka	Kintone	LDAP	LinkedIn
LinkedIn Ads	MailChimp	MariaDB	Marketo
MarkLogic	Microsoft Dataverse	Microsoft Entra ID	Microsoft Exchange
Microsoft OneDrive	Microsoft Planner	Microsoft Project	Microsoft Teams
Monday.com	MongoDB	MYOB AccountRight	MySQL
nCino	Neo4J	NetSuite	OData
Odoo	Office 365	Okta	OneNote
Oracle	Oracle Eloqua	Oracle Financials Cloud	Oracle HCM Cloud
Oracle Sales	Oracle SCM	Oracle Service Cloud	Outreach.io
Parquet	Paylocity	PayPal	Phoenix
PingOne	Pinterest	Pipedrive	PostgreSQL
Power BI XMLA	Presto	Quickbase	QuickBooks
QuickBooks Online	QuickBooks Time	Raisers Edge NXT	Reckon
Reckon Accounts Hosted	Redis	Redshift	REST
RSS	Sage 200	Sage 300	Sage 50 UK
Sage Cloud Accounting	Sage Intacct	Salesforce	Salesforce Data Cloud
Salesforce Financial Service Cloud	Salesforce Marketing	Salesforce Marketing Cloud Account Engagement	Salesforce Pardot
Salesloft	SAP	SAP Ariba Procurement	SAP Ariba Source
SAP Business One	SAP BusinessObjects BI	SAP ByDesign	SAP Concur
SAP Fieldglass	SAP HANA	SAP HANA XS Advanced	SAP Hybris C4C
SAP Netweaver Gateway	SAP SuccessFactors	SAS Data Sets	SAS xpt
SendGrid	ServiceNow	SFTP	SharePoint
SharePoint Excel Services	ShipStation	Shopify	SingleStore
Slack	Smartsheet	Snapchat Ads	Snowflake
Spark	Splunk	SQL Analysis Services	SQL Server
Square	Stripe	Sugar CRM	SuiteCRM
SurveyMonkey	Sybase	Sybase IQ	Tableau CRM Analytics
Tally	TaxJar	Teradata	Tier1
TigerGraph	Trello	Trino	Twilio
Twitter	Twitter Ads	Veeva CRM	Veeva Vault
Wave Financial	WooCommerce	WordPress	Workday
xBase	Xero	XML	YouTube Analytics
Zendesk	Zoho Books	Zoho Creator	Zoho CRM
Zoho Inventory	Zoho Projects	Zuora	... Dozens More

WIKIPEDIA Context: Cloud computing is defined by ISO as "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand," commonly termed "the cloud."

== Key Attributes (NIST Defined) == NIST specified five foundational characteristics for cloud environments in 2011:

• On-demand self-service: Users provision capabilities (e.g., compute time, storage) autonomously without needing provider personnel intervention.
• Broad network access: Services are universally reachable via standard network protocols, supporting diverse client devices (mobiles, laptops, etc.).
• Resource pooling: Provider resources are aggregated in a multi-tenant architecture, dynamically allocating and reallocating capacity based on current user demand.
• Rapid elasticity: Capabilities can scale up or down extremely quickly, sometimes automatically, matching fluctuating demand. To the consumer, this capacity often appears limitless.
• Measured service: Resource consumption (storage, processing, bandwidth) is automatically tracked, controlled, and reported, ensuring provider and consumer transparency regarding utilization.

ISO later updated and expanded this foundational list by 2023.

== Historical Context ==

The genesis of cloud computing traces back to the 1960s with the adoption of time-sharing concepts, often facilitated through Remote Job Entry (RJE). Mainframe operations, where users submitted batch jobs to human operators, dominated this era. The focus was squarely on optimizing infrastructure, platform, and applications to maximize computational efficiency for a larger user base.

The actual "cloud" graphical metaphor, representing virtualized services, was first utilized in 1994 by General Magic for mapping locations accessible by mobile agents within their Telescript environment. David Hoffman, a General Magic communications professional, is credited with adapting the term from its established use in telecommunications and networking. The phrase "cloud computing" gained significant traction in 1996 when Compaq Computer Corporation outlined a business strategy centered around the future of the Internet and computing capabilities.