{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"google-drive-mcp-server","slug":"google-drive-mcp-server","name":"Google Drive MCP Server","type":"mcp","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive","page_url":"https://unfragile.ai/google-drive-mcp-server","categories":["mcp-servers","documentation"],"tags":["google-drive","storage","official"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"google-drive-mcp-server__cap_0","uri":"capability://search.retrieval.mcp.compliant.google.drive.file.search.with.semantic.filtering","name":"mcp-compliant google drive file search with semantic filtering","description":"Exposes Google Drive's native search API through the Model Context Protocol's tool interface, allowing LLM clients to query files by name, metadata, and MIME type without direct API credentials. Implements MCP's tool registration pattern to translate natural language search intents into Google Drive query syntax, handling pagination and result filtering server-side before returning structured file metadata to the client.","intents":["Find a specific document in my Google Drive without manually browsing folders","Search for all spreadsheets modified in the last week across my entire Drive","Locate files by owner, shared status, or custom properties programmatically","Build an LLM agent that can autonomously discover relevant documents for a task"],"best_for":["LLM application developers building AI agents with document access","Teams integrating Google Drive as a knowledge source for Claude/GPT applications","Developers migrating from direct Google Drive API calls to MCP-based architectures"],"limitations":["Search results limited to files the authenticated user has access to — no cross-organizational discovery","Query complexity constrained by Google Drive's native query language — advanced boolean logic not supported","Pagination handled server-side; clients cannot directly control result set size or offset","Real-time search latency depends on Google Drive API response times (typically 500ms-2s)"],"requires":["Google Cloud project with Google Drive API enabled","OAuth 2.0 credentials (service account or user credentials) with drive.readonly scope","MCP client compatible with TypeScript/Node.js servers","Network access to Google Drive API endpoints"],"input_types":["search query string (e.g., 'name contains \"budget\" and mimeType=\"application/vnd.google-apps.spreadsheet\"')","filter parameters (owner, shared status, date range)"],"output_types":["structured JSON array of file metadata (id, name, mimeType, createdTime, modifiedTime, owners, permissions)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_1","uri":"capability://data.processing.analysis.google.docs.content.extraction.with.formatting.preservation","name":"google docs content extraction with formatting preservation","description":"Retrieves the full text content of Google Docs documents through the Google Drive API, converting Google's proprietary document format into plain text or structured markdown while preserving document hierarchy (headings, lists, tables). Implements streaming content retrieval to handle large documents efficiently, with server-side caching to reduce redundant API calls for frequently accessed documents.","intents":["Extract the full text of a Google Doc to feed into an LLM for summarization or analysis","Build a RAG system that indexes Google Docs content for semantic search","Retrieve document structure (headings, sections) to understand document organization programmatically","Sync Google Docs content into a local knowledge base or vector database"],"best_for":["AI teams building RAG pipelines over Google Workspace documents","Developers creating LLM-powered document analysis tools","Organizations migrating Google Docs content to external knowledge systems"],"limitations":["Formatting information (bold, italic, colors) is not preserved — output is plain text or basic markdown","Comments, suggestions, and revision history are not extracted — only published document content","Large documents (>10MB) may timeout; chunking must be handled by the client","Tables are converted to text representation; complex table layouts may lose structural fidelity","Images embedded in documents are not extracted — only alt text if available"],"requires":["Google Cloud project with Google Drive API v3 enabled","OAuth 2.0 credentials with drive.readonly scope","Document ID of the target Google Doc (obtainable via search capability)","MCP client with support for streaming or large response payloads"],"input_types":["Google Doc file ID (string)","optional: format preference (plain text or markdown)"],"output_types":["plain text or markdown string with document content","optional: structured JSON with document metadata (title, author, last modified, word count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_2","uri":"capability://data.processing.analysis.google.sheets.data.extraction.with.schema.inference","name":"google sheets data extraction with schema inference","description":"Retrieves data from Google Sheets as structured JSON or CSV, automatically inferring column types and handling sparse data. Uses the Google Sheets API to fetch cell ranges with formatting metadata, then transforms the raw grid into columnar format with optional type coercion (dates, numbers, booleans). Supports filtering and sorting server-side to reduce payload size for large sheets.","intents":["Extract a Google Sheet as a structured dataset for LLM analysis or processing","Automatically infer schema from a sheet and convert it to JSON for downstream tools","Build an LLM agent that can read and interpret tabular data from shared sheets","Sync Google Sheets data into a vector database or analytics pipeline"],"best_for":["Data analysts building LLM-powered insights from Google Sheets","Teams using Sheets as a lightweight database for AI applications","Developers integrating Sheets data into multi-step LLM workflows"],"limitations":["Type inference is heuristic-based; ambiguous columns (e.g., '123' could be string or number) require manual specification","Formulas are evaluated to their cached values; dynamic formula results may be stale if sheet hasn't been recalculated","Large sheets (>100k rows) require pagination; full-sheet extraction is not practical","Formatting (colors, fonts, conditional formatting) is not preserved in output","Merged cells are flattened; cell merging information is lost in JSON/CSV output"],"requires":["Google Cloud project with Google Sheets API v4 enabled","OAuth 2.0 credentials with sheets.readonly scope","Sheet ID and range specification (e.g., 'Sheet1!A1:D100')","MCP client capable of handling structured JSON responses"],"input_types":["Google Sheet file ID (string)","sheet name or range (e.g., 'Sheet1' or 'Sheet1!A1:D100')","optional: output format (JSON, CSV, or raw grid)"],"output_types":["JSON array of objects with inferred column types","CSV string with header row","raw grid representation with cell coordinates and values"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_3","uri":"capability://data.processing.analysis.google.slides.content.and.metadata.retrieval","name":"google slides content and metadata retrieval","description":"Extracts text content, speaker notes, and slide structure from Google Slides presentations via the Google Slides API, converting slide hierarchy into a traversable outline format. Handles both text-based content and metadata (slide titles, notes, layout information) while optionally exporting slide images as base64-encoded data for multimodal LLM processing. Implements lazy loading to avoid fetching all slides upfront.","intents":["Extract speaker notes from a presentation for LLM-based summarization","Build an LLM agent that can navigate and understand slide content programmatically","Convert presentation content into a structured outline for document generation","Analyze presentation structure and content for accessibility or compliance checking"],"best_for":["Developers building LLM tools for presentation analysis and summarization","Teams automating presentation content extraction for knowledge management","Accessibility teams auditing presentation content programmatically"],"limitations":["Text extraction is limited to text boxes and shapes; text in images or embedded objects is not extracted","Slide images are optional and require additional API calls; base64 encoding adds significant payload size","Complex layouts with overlapping text boxes may produce out-of-order text extraction","Animations and transitions are not captured; only final slide state is available","Speaker notes are extracted as plain text; formatting within notes is not preserved"],"requires":["Google Cloud project with Google Slides API enabled","OAuth 2.0 credentials with presentations.readonly scope","Presentation ID (obtainable via Drive search)","MCP client with support for large JSON payloads if slide images are included"],"input_types":["Google Slides presentation ID (string)","optional: slide range (e.g., slides 1-5)","optional: include_images flag (boolean)"],"output_types":["JSON structure with slide hierarchy: [{slide_number, title, text_content, speaker_notes, layout_type, optional: image_base64}]","plain text outline of presentation structure"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_4","uri":"capability://search.retrieval.google.drive.folder.traversal.with.recursive.listing","name":"google drive folder traversal with recursive listing","description":"Lists contents of a Google Drive folder with optional recursive traversal to build a complete folder tree structure. Uses the Google Drive API's file list endpoint with parent folder filtering, implementing depth-first or breadth-first traversal patterns to map folder hierarchies. Returns structured metadata for each file/folder (id, name, mimeType, size, permissions) enabling clients to understand Drive organization without manual navigation.","intents":["Discover all documents in a folder hierarchy for bulk processing or indexing","Build a file tree representation of a Drive folder for LLM-based navigation","Audit folder structure and permissions across a Drive organization","Implement a folder-aware search that scopes results to specific directory trees"],"best_for":["Developers building file management tools with LLM interfaces","Teams indexing entire Drive folder structures for knowledge management","Organizations auditing Drive organization and access patterns"],"limitations":["Deep recursion (>10 levels) may hit API rate limits; pagination required for large folder trees","Shared folders and shortcuts create ambiguity in tree structure; circular references possible","Permission information is simplified (owner/editor/viewer); granular permission details not included","Folder size calculation requires summing all child files; not provided directly by API","Soft-deleted files are not included; only active files appear in listings"],"requires":["Google Cloud project with Google Drive API enabled","OAuth 2.0 credentials with drive.readonly scope","Folder ID to start traversal (root folder ID for entire Drive)","MCP client with support for recursive data structures or pagination"],"input_types":["folder ID (string)","optional: max_depth (integer, default unlimited)","optional: include_shared_drives (boolean)"],"output_types":["nested JSON tree structure: {folder_id, name, children: [{file_id, name, mimeType, size, ...}]}","flat JSON array of all files with parent_id for client-side tree building"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_5","uri":"capability://tool.use.integration.mcp.resource.exposure.for.drive.file.metadata.and.content","name":"mcp resource exposure for drive file metadata and content","description":"Exposes Google Drive files as MCP resources, allowing LLM clients to reference files by URI (e.g., 'gdrive://file-id') and retrieve their metadata or content on-demand. Implements MCP's resource protocol to provide file handles that clients can pass between tools, enabling workflows where one tool's output (a file ID) becomes another tool's input without explicit serialization. Supports resource templates for common patterns (e.g., 'gdrive://folder/{folder-id}/files').","intents":["Reference a Google Drive file in an LLM prompt without embedding its full content","Chain multiple tools together where one tool discovers a file and another reads it","Enable LLM clients to maintain references to Drive files across multiple conversation turns","Build resource-aware workflows that treat Drive files as first-class entities"],"best_for":["LLM application developers building multi-step workflows with Drive integration","Teams using MCP clients that leverage resource references for efficiency","Developers building conversational interfaces that maintain file context"],"limitations":["Resource URIs are opaque to clients; no direct file access without MCP server","Resource templates require server-side implementation; not all Drive patterns may be supported","Content retrieval through resources is lazy; clients must explicitly request content","Resource expiration/invalidation is not handled; stale references may fail silently"],"requires":["MCP client that supports resource protocol (most modern MCP clients do)","Google Drive API credentials with appropriate scopes","Understanding of MCP resource URI format and templates"],"input_types":["resource URI string (e.g., 'gdrive://file-id' or 'gdrive://folder/folder-id/files')"],"output_types":["resource metadata (name, mimeType, size, created/modified timestamps)","resource content (file data, text, or structured format depending on file type)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_6","uri":"capability://tool.use.integration.oauth.2.0.credential.management.with.automatic.token.refresh","name":"oauth 2.0 credential management with automatic token refresh","description":"Manages Google OAuth 2.0 authentication for the MCP server, handling credential storage, token refresh, and expiration. Implements automatic token refresh before expiration to prevent mid-request failures, with fallback to credential re-authentication if refresh fails. Supports both service account credentials (for server-to-server access) and user credentials (for user-delegated access), with secure credential storage using environment variables or encrypted local storage.","intents":["Authenticate the MCP server with Google Drive without manual token management","Ensure API calls don't fail due to expired credentials","Support both service account and user-delegated authentication flows","Securely store and rotate credentials in production environments"],"best_for":["Developers deploying Google Drive MCP servers in production","Teams requiring long-running servers with automatic credential refresh","Organizations using both service accounts and user credentials"],"limitations":["Service account credentials require a Google Cloud project and service account setup","User credentials require initial OAuth consent flow; cannot be fully automated","Token refresh adds ~100-200ms latency to first API call after expiration","Credential storage security depends on environment configuration; no built-in encryption","Scope changes require re-authentication; incremental scope addition is not supported"],"requires":["Google Cloud project with Google Drive API enabled","OAuth 2.0 credentials (service account JSON or user credentials JSON)","Environment variables or secure credential storage configured","Node.js runtime with access to file system or environment"],"input_types":["credential file path or environment variable reference","optional: scopes array (defaults to drive.readonly)"],"output_types":["authenticated Google API client ready for Drive API calls","token refresh status and expiration information"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_7","uri":"capability://tool.use.integration.google.drive.api.error.handling.with.retry.logic.and.rate.limiting","name":"google drive api error handling with retry logic and rate limiting","description":"Implements resilient error handling for Google Drive API calls with exponential backoff retry logic for transient failures (rate limits, timeouts, temporary service errors). Distinguishes between retryable errors (429, 500, 503) and permanent failures (401, 403, 404) to avoid wasting retries on unrecoverable errors. Tracks API quota usage and implements client-side rate limiting to prevent hitting Google's quotas, with configurable backoff strategies.","intents":["Ensure API calls succeed despite transient Google Drive service issues","Avoid quota exhaustion by implementing client-side rate limiting","Provide meaningful error messages to clients when Drive operations fail","Build resilient LLM workflows that gracefully handle Drive API failures"],"best_for":["Developers building production MCP servers with high reliability requirements","Teams with heavy Drive API usage concerned about quota management","Applications requiring graceful degradation when Drive is unavailable"],"limitations":["Retry logic adds latency (exponential backoff can delay responses by 10-30 seconds)","Rate limiting is client-side only; no coordination across multiple server instances","Quota tracking is approximate; actual quota usage may differ from tracked values","Permanent errors (403 Forbidden) fail immediately without retry; may confuse users","Retry budget is finite; repeated failures eventually exhaust retries"],"requires":["Google Drive API credentials with appropriate scopes","Configuration for retry strategy (max retries, backoff multiplier)","Optional: quota tracking configuration"],"input_types":["API call configuration (method, parameters)","optional: retry strategy overrides"],"output_types":["API response on success","structured error object with error code, message, and retry recommendation on failure"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_8","uri":"capability://tool.use.integration.mcp.tool.schema.generation.from.google.drive.api.capabilities","name":"mcp tool schema generation from google drive api capabilities","description":"Automatically generates MCP tool schemas that describe available Google Drive operations (search, read, list) with input parameters, output types, and descriptions. Uses introspection of the Google Drive API to build tool definitions that MCP clients can discover and invoke, with parameter validation and type coercion. Implements schema versioning to support API changes without breaking clients.","intents":["Enable MCP clients to discover available Google Drive operations automatically","Provide type-safe tool invocation with parameter validation","Generate documentation for available Drive operations from schema","Support schema evolution as Google Drive API changes"],"best_for":["MCP client developers building dynamic tool discovery","Teams requiring self-documenting Drive integration","Developers maintaining MCP servers across API versions"],"limitations":["Schema generation is static; runtime parameter validation may differ from schema","Complex parameter types (nested objects, unions) may not map cleanly to JSON schema","Schema versioning requires manual updates; no automatic API change detection","Descriptions are generated from API docs; may be generic or incomplete"],"requires":["MCP SDK with schema generation support","Google Drive API documentation or introspection data","JSON schema validation library"],"input_types":["Google Drive API capability list","optional: schema customization rules"],"output_types":["JSON schema definitions for each tool","tool metadata (name, description, input/output types)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__cap_9","uri":"capability://automation.workflow.batch.file.operations.with.transactional.semantics","name":"batch file operations with transactional semantics","description":"Enables batch operations on multiple Google Drive files (e.g., bulk read, bulk metadata update) with transactional guarantees where possible. Implements request batching to reduce API call overhead, grouping multiple operations into single batch requests. Provides rollback semantics for write operations: if any operation in a batch fails, all changes are reverted (where supported by Drive API).","intents":["Read multiple documents in a single batch operation to reduce latency","Update metadata for multiple files atomically","Build efficient bulk processing workflows for Drive content","Implement safe multi-file operations with rollback on failure"],"best_for":["Developers building bulk document processing tools","Teams requiring atomic multi-file operations","Applications processing large numbers of Drive files"],"limitations":["Transactional semantics are limited; Google Drive API doesn't support true ACID transactions","Batch size is limited by API (typically 100 requests per batch); very large batches require multiple rounds","Rollback is not automatic; client must implement rollback logic for failed operations","Batch latency is higher than individual requests for small batches (overhead not amortized)","Some operations (e.g., sharing changes) cannot be batched"],"requires":["Google Drive API with batch request support","MCP client capable of handling batch operation results","Configuration for batch size and timeout"],"input_types":["array of file operations (read, update, delete)","optional: rollback strategy (all-or-nothing vs best-effort)"],"output_types":["array of operation results with status (success/failure) for each","optional: rollback status if rollback was requested"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-drive-mcp-server__headline","uri":"capability://tool.use.integration.mcp.server.for.google.drive.file.management","name":"mcp server for google drive file management","description":"An official Model Context Protocol (MCP) server designed for managing Google Drive files, enabling users to search, read, and access Google Docs, Sheets, and Slides seamlessly.","intents":["best MCP server for Google Drive","MCP server for file management","how to manage Google Drive files with MCP","MCP server for accessing Google Docs","MCP solutions for Google Drive integration"],"best_for":["developers looking to integrate Google Drive with MCP"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":77,"verified":false,"data_access_risk":"high","permissions":["Google Cloud project with Google Drive API enabled","OAuth 2.0 credentials (service account or user credentials) with drive.readonly scope","MCP client compatible with TypeScript/Node.js servers","Network access to Google Drive API endpoints","Google Cloud project with Google Drive API v3 enabled","OAuth 2.0 credentials with drive.readonly scope","Document ID of the target Google Doc (obtainable via search capability)","MCP client with support for streaming or large response payloads","Google Cloud project with Google Sheets API v4 enabled","OAuth 2.0 credentials with sheets.readonly scope"],"failure_modes":["Search results limited to files the authenticated user has access to — no cross-organizational discovery","Query complexity constrained by Google Drive's native query language — advanced boolean logic not supported","Pagination handled server-side; clients cannot directly control result set size or offset","Real-time search latency depends on Google Drive API response times (typically 500ms-2s)","Formatting information (bold, italic, colors) is not preserved — output is plain text or basic markdown","Comments, suggestions, and revision history are not extracted — only published document content","Large documents (>10MB) may timeout; chunking must be handled by the client","Tables are converted to text representation; complex table layouts may lose structural fidelity","Images embedded in documents are not extracted — only alt text if available","Type inference is heuristic-based; ambiguous columns (e.g., '123' could be string or number) require manual specification","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.5900000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=google-drive-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=google-drive-mcp-server"}},"signature":"EKqGQlLDbwZbfje5aGHaBnItJ72ISZHTnk3m6cM0VLHMnC3QqlsqrqNhcne7ZrUFWbUGtPu4f4V6CGUtVrmyDg==","signedAt":"2026-06-21T09:57:20.659Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-drive-mcp-server","artifact":"https://unfragile.ai/google-drive-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=google-drive-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}