Google Drive MCP Server

MCP ServerFree

Search, read, and manage Google Drive files via MCP.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp-compliant google drive file search with semantic filtering

Medium confidence

Exposes a standardized MCP tool that searches Google Drive using the Google Drive API's query language, returning file metadata (name, ID, MIME type, modification date) filtered by file type, ownership, and modification recency. Implements the MCP Tools primitive to allow LLM clients to discover and invoke search with typed parameters, enabling agents to locate documents without direct API knowledge.

Solves for

Find a specific document in Google Drive by name or metadata without manually browsing foldersDiscover all spreadsheets modified in the last week for automated reporting workflowsSearch for files by MIME type (Google Docs, Sheets, Slides) to filter results programmaticallyEnable an AI agent to locate context documents before processing or summarization tasks

Best for

AI agents and LLM applications that need to locate files in Google Drive as part of multi-step workflows

Teams building document-aware chatbots or knowledge assistants

Developers integrating Google Drive as a context source for MCP clients

Requires

Google Cloud project with Google Drive API enabled

OAuth 2.0 credentials (service account or user credentials) with drive.readonly or drive scope

MCP client that supports the Tools primitive (e.g., Claude Desktop, custom MCP host)

Limitations

Search results limited to files accessible by the authenticated Google account; shared drives require explicit permission configuration

Query syntax follows Google Drive API constraints — complex boolean queries may require multiple API calls

No full-text search of document contents; searches only file metadata (name, owner, modification date)

What makes it unique

Implements MCP Tools primitive with Google Drive API query language, allowing LLM clients to construct complex file searches via standardized schema-based function calling rather than direct API manipulation. Leverages Google Drive's native query syntax (e.g., 'mimeType="application/vnd.google-apps.document"') exposed through MCP's typed parameter system.

vs alternatives

Provides standardized MCP-compliant search discovery vs. raw Google Drive API SDKs, enabling any MCP client to search Drive without implementing authentication or query construction logic.

google docs content extraction with formatting preservation

Medium confidence

Reads Google Docs documents via the Google Drive API and exports content as plain text or structured format, preserving document structure (headings, lists, tables) through the Google Docs API's document structure representation. Implements MCP Resources primitive to expose documents as accessible context that LLM clients can reference by document ID, with automatic content fetching and formatting normalization.

Solves for

Retrieve the full text content of a Google Doc to use as context in an LLM promptExtract structured content from a Google Doc (headings, paragraphs, lists) for analysis or summarizationAccess a shared Google Doc without downloading it manually or copying/pasting contentEnable an AI agent to read documentation or knowledge base articles stored in Google Docs

Best for

Knowledge workers building AI assistants that reference internal documentation in Google Docs

Teams using Google Docs as a knowledge base and needing programmatic access for LLM context

Developers building document-aware agents that need to fetch and process Docs on-demand

Requires

Google Cloud project with Google Docs API enabled

OAuth 2.0 credentials with drive.readonly or docs.readonly scope

Document ID (from Google Drive file metadata or direct URL)

Limitations

Exports content as plain text or basic structured format; complex formatting (custom fonts, colors, embedded images) is not preserved

Comments, suggestions, and revision history are not included in exported content

Large documents (>10MB or >100k words) may exceed LLM context windows; requires chunking or summarization

What makes it unique

Exposes Google Docs as MCP Resources with automatic content fetching and structure preservation, allowing LLM clients to reference documents by ID and receive formatted content without manual export. Uses Google Docs API's document structure representation to reconstruct hierarchical content (headings, lists) rather than raw text extraction.

vs alternatives

Provides MCP-native document access vs. manual export or REST API calls, enabling seamless integration with LLM context management and automatic content refresh without client-side file handling.

google sheets data extraction with cell-level access

Medium confidence

Reads Google Sheets spreadsheets via the Google Sheets API and extracts cell values, formulas, and metadata (sheet names, ranges, data types) as structured JSON. Implements MCP Resources primitive to expose sheets as queryable data sources, with support for specific range selection and automatic type inference for numeric, text, and date values.

Solves for

Fetch data from a Google Sheet to use as input for LLM analysis or decision-makingExtract a specific range or table from a Sheet for processing in an AI workflowAccess shared spreadsheets for real-time data lookup without manual file downloadsEnable an agent to read configuration data, lookup tables, or datasets stored in Google Sheets

Best for

Data analysts and teams using Google Sheets as a lightweight database for AI workflows

Developers building agents that need to query structured data in Sheets

Organizations using Sheets for configuration management or lookup tables in LLM applications

Requires

Google Cloud project with Google Sheets API enabled

OAuth 2.0 credentials with spreadsheets.readonly or spreadsheets scope

Sheet ID (from Google Drive file metadata or Sheets URL)

Limitations

Formulas are returned as-is; computed values require separate API call or client-side evaluation

Large sheets (>100k rows) may exceed API quota or LLM context limits; requires pagination or filtering

Cell formatting (colors, fonts, conditional formatting) is not preserved; only values and data types are extracted

What makes it unique

Exposes Google Sheets as MCP Resources with cell-level access and type inference, allowing LLM clients to query specific ranges and receive structured JSON with automatic data type detection (numbers, dates, text) rather than raw string values. Supports both full sheet and range-based queries.

vs alternatives

Provides MCP-native spreadsheet access with type-aware data extraction vs. raw CSV export or REST API calls, enabling LLM-friendly structured data access without client-side parsing or type conversion.

google slides presentation content extraction

Medium confidence

Reads Google Slides presentations via the Google Slides API and extracts slide content (text, speaker notes, layout information) as structured JSON. Implements MCP Resources primitive to expose slides as queryable documents, with support for per-slide or full-presentation extraction and automatic text aggregation from all slide elements.

Solves for

Extract text content from a Google Slides presentation for summarization or analysisRetrieve speaker notes from slides for context in an LLM-driven presentation assistantAccess slide content programmatically to build a searchable index of presentation materialsEnable an agent to analyze presentation structure and content for quality or compliance checks

Best for

Teams building presentation analysis or summarization tools powered by LLMs

Developers creating AI assistants that need to reference or analyze slide decks

Organizations using Slides for training materials and needing programmatic content access

Requires

Google Cloud project with Google Slides API enabled

OAuth 2.0 credentials with presentations.readonly or presentations scope

Presentation ID (from Google Drive file metadata or Slides URL)

Limitations

Images and embedded media are not extracted; only text content from text boxes and speaker notes

Slide layout and visual design information is minimal; focus is on text content only

Speaker notes are extracted but not associated with specific slide elements

What makes it unique

Exposes Google Slides as MCP Resources with automatic text aggregation from all slide elements (text boxes, speaker notes, shapes), allowing LLM clients to analyze presentation content without manual export or image processing. Structures slide data hierarchically by slide and element type.

vs alternatives

Provides MCP-native presentation access with text extraction vs. manual export or image-based OCR, enabling efficient LLM-driven analysis of slide content without visual processing overhead.

folder hierarchy traversal and listing

Medium confidence

Lists files and subfolders within a Google Drive folder using the Google Drive API's children query, returning hierarchical folder structure with file metadata. Implements MCP Tools primitive to allow LLM clients to discover folder contents recursively, with support for filtering by file type and pagination for large folders. Enables agents to navigate Drive structure without prior knowledge of file IDs.

Solves for

Explore the structure of a Google Drive folder to understand available documentsList all files in a folder matching a specific type (e.g., all Google Docs)Recursively discover files in nested folder hierarchies for comprehensive searchesEnable an agent to browse Drive structure and select relevant documents for processing

Best for

AI agents that need to discover available documents before processing

Teams building document management interfaces powered by LLMs

Developers creating Drive explorers or knowledge base browsers

Requires

Google Cloud project with Google Drive API enabled

OAuth 2.0 credentials with drive.readonly or drive scope

Folder ID (from Google Drive or previous search results)

Limitations

Recursive traversal requires multiple API calls (one per folder level); deep hierarchies add latency

Pagination required for folders with >1000 files; requires iterative API calls

Shared drives and team drives may have different permission models; access control must be validated

What makes it unique

Implements MCP Tools for folder traversal with hierarchical discovery, allowing LLM clients to explore Drive structure via standardized function calls. Supports both shallow (single folder) and recursive (nested hierarchy) listing with automatic pagination handling.

vs alternatives

Provides MCP-native folder navigation vs. raw Drive API calls, enabling agents to discover documents dynamically without pre-computed file lists or manual folder ID lookup.

oauth 2.0 authentication and credential management

Medium confidence

Manages Google OAuth 2.0 authentication flow for Google Drive API access, handling credential exchange, token refresh, and scope negotiation. Implements MCP server-level authentication that abstracts credential management from individual tool/resource calls, storing tokens securely and automatically refreshing expired credentials. Supports both user-delegated (OAuth 2.0 authorization code flow) and service account authentication patterns.

Solves for

Authenticate the MCP server with Google Drive API using OAuth 2.0 without exposing credentials to clientsAutomatically refresh expired access tokens to maintain continuous API accessManage multiple authentication scopes (drive.readonly, docs.readonly, sheets.readonly) for different capabilitiesEnable secure credential storage and rotation for production deployments

Best for

Teams deploying Google Drive MCP servers in production environments

Developers building multi-user systems where credential isolation is required

Organizations with security policies requiring OAuth 2.0 and token refresh

Requires

Google Cloud project with OAuth 2.0 credentials configured

Client ID and client secret (for user-delegated flow) or service account key file

Redirect URI configured in Google Cloud Console (for user-delegated flow)

Limitations

User-delegated OAuth requires interactive authorization; service accounts require key file management

Token refresh adds ~100-200ms latency on first request after expiration

Credential storage must be secured externally (environment variables, secret managers); server does not provide encryption

What makes it unique

Implements MCP server-level OAuth 2.0 credential management with automatic token refresh, abstracting authentication complexity from individual tool calls. Supports both user-delegated and service account flows, with scope-based access control for different API capabilities.

vs alternatives

Provides centralized, MCP-native authentication vs. per-tool credential handling, reducing security surface area and enabling consistent token lifecycle management across all Google Drive capabilities.

mcp protocol transport and schema validation

Medium confidence

Implements the MCP protocol layer using JSON-RPC 2.0 over stdio or HTTP transport, with automatic schema validation for tool parameters and resource requests. Handles MCP primitives (Tools, Resources, Prompts, Roots) through standardized message serialization, parameter type checking, and error handling. Exposes Google Drive capabilities through MCP's discovery mechanism, allowing clients to introspect available tools and resources.

Solves for

Enable any MCP-compatible client (Claude Desktop, custom hosts) to discover and invoke Google Drive toolsValidate tool parameters before API calls to catch malformed requests earlySerialize and deserialize MCP messages with proper error handling and response formattingSupport multiple transport mechanisms (stdio, HTTP) for different deployment scenarios

Best for

Developers integrating Google Drive MCP server with MCP clients (Claude Desktop, custom hosts)

Teams building MCP ecosystems with multiple servers and clients

Organizations standardizing on MCP for tool integration across AI applications

Requires

MCP-compatible client with JSON-RPC 2.0 support

Transport mechanism configured (stdio for local, HTTP for remote)

MCP SDK (TypeScript or Python) for server implementation

Limitations

JSON-RPC 2.0 overhead adds ~10-50ms per request for serialization/deserialization

Schema validation is strict; requests with unexpected parameters are rejected

Transport layer (stdio, HTTP) must be configured correctly; misconfiguration breaks client-server communication

What makes it unique

Implements full MCP protocol stack with JSON-RPC 2.0 serialization, schema validation, and transport abstraction, enabling standardized client-server communication. Exposes Google Drive capabilities through MCP's discovery mechanism (tools/list, resources/list) for automatic client introspection.

vs alternatives

Provides MCP-native protocol implementation vs. custom REST APIs, enabling interoperability with any MCP client and automatic capability discovery without custom integration code.

error handling and api quota management

Medium confidence

Implements error handling for Google Drive API failures (rate limits, authentication errors, not-found errors) with automatic retry logic and exponential backoff. Tracks API quota usage and provides feedback to clients when rate limits are approached, preventing cascading failures. Maps Google Drive API errors to MCP error responses with descriptive messages and recovery suggestions.

Solves for

Handle transient API failures gracefully with automatic retry and backoffPrevent rate limit errors by tracking quota usage and throttling requestsProvide clear error messages to clients when operations failEnable agents to implement fallback strategies when API limits are reached

Best for

Production deployments where API reliability and quota management are critical

High-volume applications making frequent Google Drive API calls

Teams building resilient agents that need graceful degradation under load

Requires

Google Cloud project with appropriate API quota configured

Error handling configuration (retry count, backoff strategy)

Monitoring/logging infrastructure to track quota usage

Limitations

Retry logic adds latency (exponential backoff can delay responses by seconds)

Quota tracking is approximate; actual limits may vary based on account type and API tier

Some errors (authentication failures, permission denied) are not retryable; require manual intervention

What makes it unique

Implements MCP-aware error handling with automatic retry and exponential backoff for transient failures, combined with quota tracking to prevent rate limit errors. Maps Google Drive API errors to MCP error responses with actionable recovery suggestions.

vs alternatives

Provides built-in resilience vs. raw API calls, reducing client-side error handling complexity and enabling transparent retry logic without exposing quota management details to callers.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with Google Drive MCP Server, ranked by overlap. Discovered automatically through the match graph.

MCP Server21

Google Drive

** - File access and search capabilities for Google Drive.

full-text search across google drive with semantic query supportfile content retrieval and export with format conversiongoogle drive file listing and traversal with hierarchical navigationfile upload and creation with metadata assignment

4 shared capabilities

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data extraction and structured output generationcontextual search and retrieval within workspace documents

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google_workspace_mcp

Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace / G Suite MCP Server & CLI Tool

google drive file operations with search, metadata extraction, and permission management

1 shared capability

Agent58

awesome-n8n-templates

280+ free n8n automation templates — ready-to-use workflows for Gmail, Telegram, Slack, Discord, WhatsApp, Google Drive, Notion, OpenAI, and more. AI agents, RAG chatbots, email automation, social media, DevOps, and document processing. The largest open-source n8n template collection.

google drive and sheets integration with document processing and data sync

1 shared capability

MCP Server24

Google PSE/CSE

** - A Model Context Protocol (MCP) server providing access to Google Programmable Search Engine (PSE) and Custom Search Engine (CSE).

mcp-native web search via google custom search api

1 shared capability

Best For

✓AI agents and LLM applications that need to locate files in Google Drive as part of multi-step workflows
✓Teams building document-aware chatbots or knowledge assistants
✓Developers integrating Google Drive as a context source for MCP clients
✓Knowledge workers building AI assistants that reference internal documentation in Google Docs
✓Teams using Google Docs as a knowledge base and needing programmatic access for LLM context
✓Developers building document-aware agents that need to fetch and process Docs on-demand
✓Data analysts and teams using Google Sheets as a lightweight database for AI workflows
✓Developers building agents that need to query structured data in Sheets

Known Limitations

⚠Search results limited to files accessible by the authenticated Google account; shared drives require explicit permission configuration
⚠Query syntax follows Google Drive API constraints — complex boolean queries may require multiple API calls
⚠No full-text search of document contents; searches only file metadata (name, owner, modification date)
⚠Pagination handled via pageToken; large result sets require iterative API calls, adding latency
⚠Exports content as plain text or basic structured format; complex formatting (custom fonts, colors, embedded images) is not preserved
⚠Comments, suggestions, and revision history are not included in exported content

Requirements

Google Cloud project with Google Drive API enabledOAuth 2.0 credentials (service account or user credentials) with drive.readonly or drive scopeMCP client that supports the Tools primitive (e.g., Claude Desktop, custom MCP host)Network connectivity to Google Drive API endpointsGoogle Cloud project with Google Docs API enabledOAuth 2.0 credentials with drive.readonly or docs.readonly scopeDocument ID (from Google Drive file metadata or direct URL)MCP client supporting Resources primitive

Input / Output

Accepts: text (search query string), structured parameters (MIME type filter, owner filter, modification date range), text (Google Docs document ID), text (Sheet ID and optional range specification, e.g., 'Sheet1!A1:D10'), text (Presentation ID and optional slide range), text (folder ID and optional recursion depth parameter), OAuth 2.0 authorization code (user-delegated flow) or service account key JSON, JSON-RPC 2.0 requests (tools/call, resources/read, etc.), API responses with error codes and status

Produces: JSON array of file metadata objects (id, name, mimeType, modifiedTime, owners, webViewLink), text (plain text content), structured JSON (document structure with headings, paragraphs, lists, tables), structured JSON (rows and columns with typed values), CSV-like format (for compatibility with downstream tools), structured JSON (slides with text content, speaker notes, and layout metadata), plain text (aggregated text from all slides), JSON array of file/folder objects with metadata (id, name, mimeType, parents, webViewLink), access token (used internally for API calls), refresh token (for token renewal), JSON-RPC 2.0 responses with tool results or resource content, MCP error responses with error codes, messages, and recovery suggestions

UnfragileRank

Adoption70%(30% weight)

Quality23%(25% weight)

Ecosystem49%(25% weight)

Match Graph10%(15% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit Google Drive MCP Server→

About

Official MCP server for Google Drive file management. Provides tools to search files, read document contents, list folder contents, and access Google Docs, Sheets, and Slides resources.

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Capabilities8 decomposed

mcp-compliant google drive file search with semantic filtering

Medium confidence

Solves for

Best for

AI agents and LLM applications that need to locate files in Google Drive as part of multi-step workflows

Teams building document-aware chatbots or knowledge assistants

Developers integrating Google Drive as a context source for MCP clients

Requires

Google Cloud project with Google Drive API enabled

OAuth 2.0 credentials (service account or user credentials) with drive.readonly or drive scope

MCP client that supports the Tools primitive (e.g., Claude Desktop, custom MCP host)

Limitations

Search results limited to files accessible by the authenticated Google account; shared drives require explicit permission configuration

Query syntax follows Google Drive API constraints — complex boolean queries may require multiple API calls

No full-text search of document contents; searches only file metadata (name, owner, modification date)

What makes it unique

vs alternatives

Provides standardized MCP-compliant search discovery vs. raw Google Drive API SDKs, enabling any MCP client to search Drive without implementing authentication or query construction logic.

google docs content extraction with formatting preservation

Medium confidence

Solves for

Best for

Knowledge workers building AI assistants that reference internal documentation in Google Docs

Teams using Google Docs as a knowledge base and needing programmatic access for LLM context

Developers building document-aware agents that need to fetch and process Docs on-demand

Requires

Google Cloud project with Google Docs API enabled

OAuth 2.0 credentials with drive.readonly or docs.readonly scope

Document ID (from Google Drive file metadata or direct URL)

Limitations

Exports content as plain text or basic structured format; complex formatting (custom fonts, colors, embedded images) is not preserved

Comments, suggestions, and revision history are not included in exported content

Large documents (>10MB or >100k words) may exceed LLM context windows; requires chunking or summarization

What makes it unique

vs alternatives

Provides MCP-native document access vs. manual export or REST API calls, enabling seamless integration with LLM context management and automatic content refresh without client-side file handling.

google sheets data extraction with cell-level access

Medium confidence

Solves for

Best for

Data analysts and teams using Google Sheets as a lightweight database for AI workflows

Developers building agents that need to query structured data in Sheets

Organizations using Sheets for configuration management or lookup tables in LLM applications

Requires

Google Cloud project with Google Sheets API enabled

OAuth 2.0 credentials with spreadsheets.readonly or spreadsheets scope

Sheet ID (from Google Drive file metadata or Sheets URL)

Limitations

Formulas are returned as-is; computed values require separate API call or client-side evaluation

Large sheets (>100k rows) may exceed API quota or LLM context limits; requires pagination or filtering

Cell formatting (colors, fonts, conditional formatting) is not preserved; only values and data types are extracted

What makes it unique

vs alternatives

google slides presentation content extraction

Medium confidence

Solves for

Best for

Teams building presentation analysis or summarization tools powered by LLMs

Developers creating AI assistants that need to reference or analyze slide decks

Organizations using Slides for training materials and needing programmatic content access

Requires

Google Cloud project with Google Slides API enabled

OAuth 2.0 credentials with presentations.readonly or presentations scope

Presentation ID (from Google Drive file metadata or Slides URL)

Limitations

Images and embedded media are not extracted; only text content from text boxes and speaker notes

Slide layout and visual design information is minimal; focus is on text content only

Speaker notes are extracted but not associated with specific slide elements

What makes it unique

vs alternatives

Provides MCP-native presentation access with text extraction vs. manual export or image-based OCR, enabling efficient LLM-driven analysis of slide content without visual processing overhead.

folder hierarchy traversal and listing

Medium confidence

Solves for

Best for

AI agents that need to discover available documents before processing

Teams building document management interfaces powered by LLMs

Developers creating Drive explorers or knowledge base browsers

Requires

Google Cloud project with Google Drive API enabled

OAuth 2.0 credentials with drive.readonly or drive scope

Folder ID (from Google Drive or previous search results)

Limitations

Recursive traversal requires multiple API calls (one per folder level); deep hierarchies add latency

Pagination required for folders with >1000 files; requires iterative API calls

Shared drives and team drives may have different permission models; access control must be validated

What makes it unique

vs alternatives

Provides MCP-native folder navigation vs. raw Drive API calls, enabling agents to discover documents dynamically without pre-computed file lists or manual folder ID lookup.

oauth 2.0 authentication and credential management

Medium confidence

Solves for

Best for

Teams deploying Google Drive MCP servers in production environments

Developers building multi-user systems where credential isolation is required

Organizations with security policies requiring OAuth 2.0 and token refresh

Requires

Google Cloud project with OAuth 2.0 credentials configured

Client ID and client secret (for user-delegated flow) or service account key file

Redirect URI configured in Google Cloud Console (for user-delegated flow)

Limitations

User-delegated OAuth requires interactive authorization; service accounts require key file management

Token refresh adds ~100-200ms latency on first request after expiration

Credential storage must be secured externally (environment variables, secret managers); server does not provide encryption

What makes it unique

vs alternatives

mcp protocol transport and schema validation

Medium confidence

Solves for

Best for

Developers integrating Google Drive MCP server with MCP clients (Claude Desktop, custom hosts)

Teams building MCP ecosystems with multiple servers and clients

Organizations standardizing on MCP for tool integration across AI applications

Requires

MCP-compatible client with JSON-RPC 2.0 support

Transport mechanism configured (stdio for local, HTTP for remote)

MCP SDK (TypeScript or Python) for server implementation

Limitations

JSON-RPC 2.0 overhead adds ~10-50ms per request for serialization/deserialization

Schema validation is strict; requests with unexpected parameters are rejected

Transport layer (stdio, HTTP) must be configured correctly; misconfiguration breaks client-server communication

What makes it unique

vs alternatives

Provides MCP-native protocol implementation vs. custom REST APIs, enabling interoperability with any MCP client and automatic capability discovery without custom integration code.

error handling and api quota management

Medium confidence

Solves for

Best for

Production deployments where API reliability and quota management are critical

High-volume applications making frequent Google Drive API calls

Teams building resilient agents that need graceful degradation under load

Requires

Google Cloud project with appropriate API quota configured

Error handling configuration (retry count, backoff strategy)

Monitoring/logging infrastructure to track quota usage

Limitations

Retry logic adds latency (exponential backoff can delay responses by seconds)

Quota tracking is approximate; actual limits may vary based on account type and API tier

Some errors (authentication failures, permission denied) are not retryable; require manual intervention

What makes it unique

vs alternatives

Provides built-in resilience vs. raw API calls, reducing client-side error handling complexity and enabling transparent retry logic without exposing quota management details to callers.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Google Drive MCP Server

Capabilities8 decomposed

mcp-compliant google drive file search with semantic filtering

google docs content extraction with formatting preservation

google sheets data extraction with cell-level access

google slides presentation content extraction

folder hierarchy traversal and listing

oauth 2.0 authentication and credential management

mcp protocol transport and schema validation

error handling and api quota management

Related Artifactssharing capabilities

Google Drive

GPT Workspace

eesel AI

google_workspace_mcp

awesome-n8n-templates

Google PSE/CSE

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Google Drive MCP Server

Are you the builder of Google Drive MCP Server?

Get the weekly brief

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Google Drive MCP Server

Capabilities8 decomposed

mcp-compliant google drive file search with semantic filtering

google docs content extraction with formatting preservation

google sheets data extraction with cell-level access

google slides presentation content extraction

folder hierarchy traversal and listing

oauth 2.0 authentication and credential management

mcp protocol transport and schema validation

error handling and api quota management

Related Artifactssharing capabilities

Google Drive

GPT Workspace

eesel AI

google_workspace_mcp

awesome-n8n-templates

Google PSE/CSE

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Google Drive MCP Server

Are you the builder of Google Drive MCP Server?

Get the weekly brief

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