{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-basecamp","slug":"basecamp","name":"Basecamp","type":"mcp","url":"https://github.com/georgeantonopoulos/Basecamp-MCP-Server","page_url":"https://unfragile.ai/basecamp","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-basecamp__cap_0","uri":"capability://tool.use.integration.oauth.2.0.token.based.authentication.with.automatic.refresh","name":"oauth 2.0 token-based authentication with automatic refresh","description":"Implements a complete OAuth 2.0 flow using a Flask-based web interface (oauth_app.py) that handles token exchange, local storage with expiration detection, and automatic token refresh without user intervention. The system stores tokens locally and detects expiration via get_token() and store_token() functions, automatically refreshing credentials before API calls fail, eliminating manual re-authentication cycles.","intents":["Set up secure authentication to Basecamp without storing passwords","Maintain persistent API access across multiple MCP server sessions","Automatically refresh expired tokens without interrupting workflows"],"best_for":["AI development teams using Cursor IDE or Claude Desktop with Basecamp","Developers building MCP integrations requiring OAuth flows","Organizations needing secure, token-based API access without credential storage"],"limitations":["Tokens stored locally in plaintext — no encryption at rest, requires filesystem security","OAuth flow requires browser interaction for initial authorization, cannot be fully automated","Token refresh logic is synchronous, may add latency on first API call after expiration"],"requires":["Python 3.8+","Basecamp 3 account with OAuth app credentials (client_id, client_secret)","Internet connection for OAuth redirect flow","Flask dependency for web interface"],"input_types":["OAuth authorization code from browser redirect","Basecamp API credentials (client_id, client_secret)"],"output_types":["Access token (JWT or opaque token)","Refresh token","Token expiration timestamp"],"categories":["tool-use-integration","authentication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_1","uri":"capability://tool.use.integration.mcp.protocol.bridging.with.fastmcp.framework","name":"mcp protocol bridging with fastmcp framework","description":"Wraps the Basecamp 3 REST API as a standardized Model Context Protocol (MCP) server using Anthropic's FastMCP framework (basecamp_fastmcp.py), exposing 46 tools through async function decorators that handle protocol compliance, tool registration, and request/response marshaling. The FastMCP('basecamp') instance automatically converts Python function signatures into MCP tool schemas and manages bidirectional communication with AI clients like Claude Desktop and Cursor IDE.","intents":["Enable Claude and Cursor to directly invoke Basecamp operations without custom API wrappers","Standardize Basecamp integration across multiple AI development environments","Reduce boilerplate by auto-generating MCP tool schemas from Python function signatures"],"best_for":["Teams using Claude Desktop or Cursor IDE as primary development environments","Developers building AI agents that need structured access to project management data","Organizations standardizing on MCP for tool integration across AI platforms"],"limitations":["FastMCP framework adds ~50-100ms overhead per tool invocation for protocol serialization","Tool schemas are generated at server startup — dynamic tool registration requires server restart","Limited to async/await patterns, no support for streaming responses in current implementation","Requires MCP client support — not compatible with REST-only API consumers"],"requires":["Python 3.8+","Anthropic FastMCP framework","MCP-compatible client (Claude Desktop, Cursor IDE, or custom MCP client)","Basecamp OAuth credentials for API authentication"],"input_types":["MCP tool invocation requests (JSON-RPC format)","Tool parameters matching Python function signatures"],"output_types":["MCP tool response (JSON-RPC result)","Structured data matching Basecamp API response schemas"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_10","uri":"capability://data.processing.analysis.project.enumeration.and.metadata.retrieval","name":"project enumeration and metadata retrieval","description":"Exposes get_projects() and get_project() tools that retrieve all accessible Basecamp projects or specific project details including metadata (name, description, status, members). The implementation enables AI agents to discover available projects and understand project structure before performing operations.","intents":["List all accessible Basecamp projects for user awareness","Retrieve project metadata for context before performing operations","Build project-aware AI agents that can select appropriate project context"],"best_for":["AI agents that need to discover available projects before taking action","Developers building project-aware workflows","Teams with multiple Basecamp projects needing unified access"],"limitations":["Project list is static at query time — no real-time updates if projects are created/deleted","No filtering by project status or membership — returns all accessible projects","Project metadata is basic (name, description) — no custom fields or extended attributes","No pagination for workspaces with 100+ projects — may timeout on large workspaces"],"requires":["Python 3.8+","Valid Basecamp OAuth token with read access to projects"],"input_types":["Optional project ID for specific project retrieval"],"output_types":["Project object with id, name, description, status, created_at, updated_at","Array of project objects for list operations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_2","uri":"capability://search.retrieval.cross.project.search.with.result.aggregation","name":"cross-project search with result aggregation","description":"Implements a BasecampSearch class that executes search queries across all accessible Basecamp projects simultaneously, aggregating results from multiple API endpoints and deduplicating matches. The search_basecamp() and global_search() tools support both project-scoped and workspace-wide queries, with result optimization that filters and ranks matches across todos, documents, messages, and other content types.","intents":["Find information across all Basecamp projects without manually switching contexts","Locate specific todos, documents, or messages across the entire workspace","Build AI agents that can autonomously search for context before taking actions"],"best_for":["Teams with multiple active Basecamp projects needing unified search","AI agents that require context retrieval before decision-making","Developers building knowledge retrieval systems on top of Basecamp"],"limitations":["Search latency scales with number of projects — workspace with 50+ projects may see 2-5 second delays","Basecamp API search is basic text matching, no semantic/fuzzy search capabilities","Result aggregation is client-side, requires fetching from multiple endpoints sequentially","No pagination support for large result sets — returns fixed maximum results per query"],"requires":["Python 3.8+","Valid Basecamp OAuth token with read access to projects","BasecampClient instance with authenticated HTTP session"],"input_types":["Search query string (text)","Optional project_id to scope search to single project"],"output_types":["Aggregated list of matching items with type (todo, document, message, etc.)","Item metadata including project_id, creator, timestamp"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_3","uri":"capability://automation.workflow.todo.list.and.item.crud.with.state.management","name":"todo list and item crud with state management","description":"Provides complete todo lifecycle management through get_todolists(), get_todos(), create_todo(), update_todo(), delete_todo(), complete_todo(), and uncomplete_todo() tools that map directly to Basecamp 3 API endpoints. The implementation handles todo state transitions (pending → completed → pending) and supports bulk operations, with each tool accepting structured parameters for title, description, due dates, and assignee information.","intents":["Create and manage todos programmatically from AI agents without manual UI interaction","Bulk update todo status across multiple items based on external conditions","Retrieve todo lists and filter by status, assignee, or due date for reporting"],"best_for":["AI agents automating task creation and status updates in Basecamp","Developers building custom todo workflows that extend Basecamp functionality","Teams integrating Basecamp todos with external automation systems"],"limitations":["No batch CRUD operations — each todo requires a separate API call, limiting bulk operations to ~10-20 items per second","State transitions are simple (complete/uncomplete) — no support for custom workflow states","No conflict detection for concurrent updates — last write wins, potential data loss in high-concurrency scenarios","Due date handling is timezone-naive, may cause issues for distributed teams"],"requires":["Python 3.8+","Valid Basecamp OAuth token with write access to target project","Project ID and optional todo list ID for scoped operations"],"input_types":["Todo list ID (string)","Todo title, description, due date, assignee (structured JSON)","Todo ID for update/delete operations"],"output_types":["Todo object with id, title, status, due_date, assignee, created_at, updated_at","Array of todo objects for list operations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_4","uri":"capability://automation.workflow.kanban.board.management.with.card.and.column.operations","name":"kanban board management with card and column operations","description":"Exposes card table (Kanban board) functionality through get_card_table(), get_columns(), get_cards(), create_card(), update_card(), move_card(), create_column(), update_column(), and move_column() tools that manage board structure and card positioning. The implementation supports hierarchical card organization with card steps (sub-tasks) via get_card_steps() and create_card_step(), enabling multi-level task breakdown within a single card table.","intents":["Programmatically move cards between columns based on external triggers or AI decisions","Create and organize cards with sub-tasks for complex project workflows","Retrieve board state for reporting, analytics, or AI-driven workflow optimization"],"best_for":["Teams using Basecamp card tables as primary project tracking mechanism","AI agents that need to update board state based on external events or decisions","Developers building custom Kanban workflows on top of Basecamp"],"limitations":["Card positioning is column-based only — no support for custom ordering within columns, relies on Basecamp's default ordering","No bulk move operations — moving multiple cards requires sequential API calls","Card steps (sub-tasks) are read-only in some operations, limited update capabilities","No support for custom card fields or metadata beyond title and description"],"requires":["Python 3.8+","Valid Basecamp OAuth token with write access to card table","Card table ID and project ID for operations"],"input_types":["Card table ID (string)","Card title, description, assignee (structured JSON)","Column ID for card placement","Card ID and target column ID for move operations"],"output_types":["Card object with id, title, status, assignee, column_id, created_at","Column object with id, name, card_count","Card step (sub-task) objects with completion status"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_5","uri":"capability://memory.knowledge.document.and.file.retrieval.with.metadata.extraction","name":"document and file retrieval with metadata extraction","description":"Provides document access through get_documents() and related tools that retrieve document metadata, content, and file information from Basecamp projects. The implementation extracts structured metadata including creator, timestamps, and file references, enabling AI agents to index and analyze project documentation without manual file downloads.","intents":["Retrieve project documentation for AI analysis and context building","Extract metadata from documents for reporting and compliance auditing","Build searchable document indexes for knowledge retrieval systems"],"best_for":["Teams using Basecamp as document repository for project knowledge","AI agents that need to analyze project documentation before making decisions","Developers building knowledge management systems on top of Basecamp"],"limitations":["Document content is returned as metadata only — actual file content requires separate download via file URLs","No full-text search on document content — only title and metadata searchable","File size limits apply per Basecamp API — large documents may not be fully retrievable","No version history access — only current document state available"],"requires":["Python 3.8+","Valid Basecamp OAuth token with read access to documents","Project ID for document retrieval"],"input_types":["Project ID (string)","Optional document ID for specific document retrieval"],"output_types":["Document object with id, title, creator, created_at, updated_at, file_url","Array of document objects for list operations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_6","uri":"capability://memory.knowledge.communication.retrieval.with.campfire.messages.and.comments","name":"communication retrieval with campfire messages and comments","description":"Exposes team communication through get_campfire_lines() for chat messages and get_comments() for item-level comments, retrieving conversation history with metadata including creator, timestamp, and content. The implementation supports querying comments on any Basecamp item (todos, documents, cards) enabling AI agents to understand discussion context and decision rationale.","intents":["Retrieve team discussions and decision context for AI analysis","Build conversation history for AI agents to understand project decisions","Extract communication patterns for team collaboration analysis"],"best_for":["Teams using Basecamp campfire for team communication","AI agents that need discussion context before making decisions","Developers building team collaboration analytics on Basecamp"],"limitations":["Campfire messages are project-scoped — no workspace-wide message search","Comment retrieval requires item ID — no bulk comment export across items","No message threading or reply tracking — flat comment structure only","Message content is plain text — no support for rich formatting or embedded media analysis"],"requires":["Python 3.8+","Valid Basecamp OAuth token with read access to communications","Project ID for campfire messages, item ID for comments"],"input_types":["Project ID for campfire messages (string)","Item ID (todo, document, card) for comment retrieval"],"output_types":["Message/comment object with id, content, creator, created_at, updated_at","Array of messages/comments for list operations"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_7","uri":"capability://data.processing.analysis.check.in.and.questionnaire.data.access","name":"check-in and questionnaire data access","description":"Provides access to Basecamp's check-in and questionnaire features through get_daily_check_ins() and get_question_answers() tools, retrieving structured responses with metadata. The implementation enables AI agents to aggregate team status updates and survey responses for reporting, trend analysis, and team health monitoring.","intents":["Retrieve daily check-in responses for team status aggregation","Extract questionnaire answers for team feedback analysis","Build team health dashboards from structured check-in data"],"best_for":["Teams using Basecamp check-ins for daily standups or status updates","Managers building team health monitoring systems","AI agents that need to understand team status before making decisions"],"limitations":["Check-in data is project-scoped — no workspace-wide aggregation","No time-series analysis built-in — requires external analytics for trend detection","Response format varies by question type — requires client-side parsing for heterogeneous data","No filtering by date range — returns all check-ins, requires client-side filtering"],"requires":["Python 3.8+","Valid Basecamp OAuth token with read access to check-ins","Project ID for check-in retrieval"],"input_types":["Project ID (string)","Optional check-in ID for specific response retrieval"],"output_types":["Check-in object with id, question, answers, respondent, created_at","Array of check-in objects for list operations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_8","uri":"capability://automation.workflow.client.specific.mcp.configuration.generation","name":"client-specific mcp configuration generation","description":"Provides automated configuration generation for different AI clients through generate_cursor_config.py and generate_claude_desktop_config.py, creating client-specific MCP server configuration files with proper paths, environment variables, and tool registration. The implementation handles client-specific requirements (Cursor vs Claude Desktop) and generates ready-to-use configuration without manual editing.","intents":["Automatically generate Cursor IDE MCP configuration for Basecamp integration","Create Claude Desktop MCP server configuration without manual setup","Reduce configuration errors by auto-generating client-specific settings"],"best_for":["Developers setting up Basecamp MCP integration in Cursor or Claude Desktop","Teams standardizing MCP configuration across multiple developers","Organizations automating MCP server deployment"],"limitations":["Configuration generation is one-time operation — manual updates required if server paths change","Client-specific configs are hardcoded for Cursor and Claude Desktop — no support for other MCP clients","Generated configs require manual placement in client-specific directories","No validation of generated configs — broken configs may not be detected until runtime"],"requires":["Python 3.8+","Cursor IDE or Claude Desktop installed (for target client)","Write access to client configuration directories"],"input_types":["Client type (cursor or claude_desktop)","MCP server path and Python executable path"],"output_types":["JSON configuration file for Cursor (in .cursor/mcp_servers.json)","JSON configuration file for Claude Desktop (in claude_desktop_config.json)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-basecamp__cap_9","uri":"capability://tool.use.integration.http.client.with.rate.limiting.and.error.handling","name":"http client with rate limiting and error handling","description":"Implements BasecampClient class with built-in rate limiting, automatic retry logic, and error handling for Basecamp API interactions. The client manages HTTP session pooling, request/response logging, and graceful degradation when rate limits are exceeded, with exponential backoff for transient failures.","intents":["Make reliable API calls to Basecamp without manual retry logic","Respect Basecamp API rate limits without overwhelming the service","Debug API failures with detailed request/response logging"],"best_for":["Developers building Basecamp integrations requiring robust error handling","Teams needing reliable API access with automatic retry and rate limiting","Builders debugging API integration issues with detailed logging"],"limitations":["Rate limiting is client-side only — no coordination across multiple client instances, potential for exceeding limits in distributed scenarios","Retry logic uses exponential backoff with fixed maximum attempts — no adaptive retry strategies","Error messages are Basecamp API responses — require client-side parsing for actionable errors","No request queuing — burst requests may exceed rate limits before backoff kicks in"],"requires":["Python 3.8+","Valid Basecamp OAuth token","requests library for HTTP operations"],"input_types":["HTTP method (GET, POST, PATCH, DELETE)","API endpoint path","Request body (JSON) for POST/PATCH operations"],"output_types":["Parsed JSON response from Basecamp API","HTTP status code and headers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Basecamp 3 account with OAuth app credentials (client_id, client_secret)","Internet connection for OAuth redirect flow","Flask dependency for web interface","Anthropic FastMCP framework","MCP-compatible client (Claude Desktop, Cursor IDE, or custom MCP client)","Basecamp OAuth credentials for API authentication","Valid Basecamp OAuth token with read access to projects","BasecampClient instance with authenticated HTTP session","Valid Basecamp OAuth token with write access to target project"],"failure_modes":["Tokens stored locally in plaintext — no encryption at rest, requires filesystem security","OAuth flow requires browser interaction for initial authorization, cannot be fully automated","Token refresh logic is synchronous, may add latency on first API call after expiration","FastMCP framework adds ~50-100ms overhead per tool invocation for protocol serialization","Tool schemas are generated at server startup — dynamic tool registration requires server restart","Limited to async/await patterns, no support for streaming responses in current implementation","Requires MCP client support — not compatible with REST-only API consumers","Project list is static at query time — no real-time updates if projects are created/deleted","No filtering by project status or membership — returns all accessible projects","Project metadata is basic (name, description) — no custom fields or extended attributes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:19.837Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=basecamp","compare_url":"https://unfragile.ai/compare?artifact=basecamp"}},"signature":"0hhjAPiaccprxDWzd+hijQ+KG44wZIKXvYnUqMF1d3uP/fQS3xmgdcQLOxlFViBCM8i4/WvPZs9537M4P+EpDw==","signedAt":"2026-06-17T03:15:50.006Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/basecamp","artifact":"https://unfragile.ai/basecamp","verify":"https://unfragile.ai/api/v1/verify?slug=basecamp","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"}}