{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-deepview-mcp","slug":"deepview-mcp","name":"DeepView MCP","type":"mcp","url":"https://github.com/ai-1st/deepview-mcp","page_url":"https://unfragile.ai/deepview-mcp","categories":["mcp-servers","app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-deepview-mcp__cap_0","uri":"capability://tool.use.integration.mcp.based.codebase.context.bridging.to.gemini","name":"mcp-based codebase context bridging to gemini","description":"Implements a Model Context Protocol server that acts as a standardized communication bridge between IDE clients (Cursor, Windsurf) and Google's Gemini API. The server registers a 'deepview' tool that receives user queries, loads preprocessed codebase content from memory, constructs prompts with full codebase context, and returns Gemini's analysis back through the MCP protocol. This eliminates the need for custom IDE plugins by leveraging the standardized MCP specification for tool registration and invocation.","intents":["Enable my IDE to query my entire codebase without custom plugin development","Use Gemini's 1M context window to analyze large codebases from within Cursor or Windsurf","Standardize how my IDE communicates with AI models for code analysis"],"best_for":["IDE users (Cursor, Windsurf) wanting native codebase analysis without custom extensions","Teams standardizing on MCP for AI tool integration across development workflows"],"limitations":["Requires MCP-compatible IDE; not compatible with VS Code without MCP support layer","Codebase must be preprocessed into single file format (via repomix or similar) before server startup","No incremental indexing — entire codebase reloaded on server restart, not suitable for real-time file watching"],"requires":["Python 3.8+","Google Gemini API key (GEMINI_API_KEY environment variable)","MCP-compatible IDE (Cursor 0.40+, Windsurf, or Claude Desktop with MCP support)","Preprocessed codebase file (generated via repomix or equivalent tool)"],"input_types":["natural language query (string)","preprocessed codebase file (text)"],"output_types":["natural language response (string)","code analysis and explanations (text)"],"categories":["tool-use-integration","ide-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_1","uri":"capability://memory.knowledge.full.codebase.context.loading.and.in.memory.indexing","name":"full-codebase context loading and in-memory indexing","description":"Loads a preprocessed codebase file (typically generated by repomix) into server memory at startup, storing the entire codebase as a single text artifact. When queries arrive, the deepview tool references this in-memory content to construct prompts for Gemini, ensuring the full codebase context is available for analysis without repeated file I/O or API calls to fetch code snippets. This pattern trades memory usage for query latency reduction and eliminates context fragmentation.","intents":["Analyze my entire codebase in a single Gemini query without splitting across multiple API calls","Get consistent answers about cross-file dependencies and architecture without context loss","Leverage Gemini's 1M token window to understand the full system at once"],"best_for":["Developers with codebases under 500K tokens (roughly 2-3M characters) that fit within Gemini's context window","Teams analyzing monorepos or tightly-coupled systems where cross-file understanding is critical"],"limitations":["Memory overhead scales linearly with codebase size; a 1M-token codebase requires ~4-8MB of RAM for storage","No incremental updates — any code changes require regenerating the preprocessed file and restarting the server","Codebases larger than Gemini's context window (1M tokens) must be manually filtered or split before preprocessing","No differential indexing or change detection; treats entire codebase as static snapshot"],"requires":["Preprocessed codebase file (text format, typically from repomix)","Sufficient RAM to hold entire codebase in memory (typically 10-50MB for most projects)","File path to preprocessed codebase passed via --codebase-file CLI argument"],"input_types":["preprocessed codebase file (text, typically .txt or .md)"],"output_types":["in-memory codebase index (string buffer)","codebase content injected into Gemini prompts (text)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_2","uri":"capability://tool.use.integration.configurable.gemini.model.selection.with.cli.parameter.binding","name":"configurable gemini model selection with cli parameter binding","description":"Exposes a --model command-line argument that allows users to select different Gemini model variants (e.g., gemini-2.0-flash-lite, gemini-1.5-pro) at server startup. The CLI parser (deepview_mcp.cli:main) passes this selection to the server initialization, which then binds the chosen model to all subsequent API calls via the google-generativeai Python SDK. This enables runtime model switching without code changes, allowing users to trade off latency, cost, and reasoning capability.","intents":["Switch between fast (gemini-2.0-flash-lite) and capable (gemini-1.5-pro) models based on query complexity","Optimize for cost by using cheaper models for simple queries and premium models for complex analysis","Test different model versions to benchmark analysis quality for my codebase"],"best_for":["Teams experimenting with different Gemini model trade-offs without code changes","Cost-conscious developers wanting to use cheaper models for routine queries","Researchers benchmarking model performance on codebase analysis tasks"],"limitations":["Model selection is static per server instance; cannot switch models mid-session without restarting","No automatic fallback if selected model is unavailable or deprecated","No cost estimation or token counting before query execution; users must track usage manually","Model-specific parameter tuning (temperature, top_p) not exposed via CLI; requires code modification"],"requires":["Valid Gemini model name (e.g., 'gemini-2.0-flash-lite', 'gemini-1.5-pro')","Active Google Gemini API key with access to selected model","CLI invocation with --model flag (e.g., deepview-mcp --model gemini-1.5-pro)"],"input_types":["model name string (CLI argument)"],"output_types":["model binding to google-generativeai client (internal state)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_3","uri":"capability://text.generation.language.prompt.construction.with.full.codebase.context.injection","name":"prompt construction with full codebase context injection","description":"The deepview tool constructs prompts by combining the user's natural language query with the entire preprocessed codebase content loaded in memory. The prompt construction logic (in deepview_mcp.server) injects the codebase as context before sending to Gemini, ensuring the model has access to all code when formulating responses. This pattern leverages Gemini's large context window to enable single-turn analysis without requiring the user to manually paste code snippets or provide file references.","intents":["Ask questions about my codebase without manually copying and pasting code files","Get answers that consider the entire system architecture, not just isolated code snippets","Reduce context-switching by staying in my IDE while analyzing code"],"best_for":["Developers analyzing codebases that fit within Gemini's 1M token context window","Teams wanting to avoid manual code snippet extraction for AI analysis"],"limitations":["Prompt size grows linearly with codebase size; very large codebases may exceed Gemini's context window","No intelligent context pruning or filtering; entire codebase is always included even if query only needs a subset","Prompt construction adds latency proportional to codebase size (file I/O + string concatenation)","No caching of constructed prompts; each query reconstructs the full prompt from scratch"],"requires":["Preprocessed codebase file loaded in server memory","Natural language query from user (string)","Gemini API access with sufficient context window for codebase + query"],"input_types":["natural language query (string)","preprocessed codebase (text, from memory)"],"output_types":["constructed prompt (string, sent to Gemini API)","Gemini response (text, returned to user)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_4","uri":"capability://automation.workflow.cli.based.server.startup.and.configuration.management","name":"cli-based server startup and configuration management","description":"Provides a command-line interface (deepview_mcp.cli:main) that parses arguments for codebase file path, model selection, and other configuration options, then initializes and starts the MCP server. The CLI handles argument validation, environment variable resolution (e.g., GEMINI_API_KEY), and server lifecycle management. This pattern enables users to start the server with a single command without editing configuration files or writing Python code.","intents":["Start the DeepView server with a single command from my terminal","Configure the server (model, codebase file) without editing Python code","Integrate server startup into shell scripts or IDE launch configurations"],"best_for":["Developers comfortable with CLI tools and shell scripting","Teams automating server startup in CI/CD or IDE configuration files","Users wanting to avoid configuration file management"],"limitations":["No interactive configuration wizard; users must know required arguments upfront","No configuration file support (YAML, JSON, .env); all settings must be passed as CLI arguments or environment variables","Limited error messages for missing arguments; users must refer to --help for guidance","No server reload or hot configuration updates; changes require restarting the server"],"requires":["Python 3.8+ with deepview-mcp package installed","GEMINI_API_KEY environment variable set","Path to preprocessed codebase file (passed via --codebase-file argument)","Optional: --model argument for model selection (defaults to gemini-2.0-flash-lite)"],"input_types":["CLI arguments (strings)","environment variables (strings)"],"output_types":["running MCP server (process)","server logs (stdout/stderr)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_5","uri":"capability://tool.use.integration.dual.mode.query.execution.cli.and.ide","name":"dual-mode query execution (cli and ide)","description":"Supports two distinct query execution paths: direct CLI usage (where users invoke the server and query it from the command line) and IDE integration (where IDEs like Cursor and Windsurf invoke the server as an MCP tool). Both paths use the same underlying deepview tool logic but differ in how queries are submitted and results are returned. The server abstracts these differences, allowing the same codebase analysis engine to serve both interactive CLI users and IDE-integrated workflows.","intents":["Query my codebase from the command line for scripting and automation","Use the same analysis engine from within my IDE without switching tools","Integrate codebase analysis into both manual workflows and automated pipelines"],"best_for":["Developers using both CLI tools and IDEs in their workflow","Teams wanting a single analysis engine for multiple interfaces","Automation engineers building scripts that analyze codebases"],"limitations":["CLI mode requires manual server startup and query submission; less integrated than IDE mode","IDE mode requires MCP-compatible IDE; not available for VS Code without MCP support","No unified query history or session management across CLI and IDE modes","Response formatting differs slightly between modes (CLI text vs IDE structured responses)"],"requires":["Running DeepView MCP server instance","For CLI mode: terminal access and ability to invoke server commands","For IDE mode: MCP-compatible IDE (Cursor, Windsurf) with server configured"],"input_types":["natural language query (string, from CLI or IDE)"],"output_types":["analysis response (text, formatted for CLI or IDE)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_6","uri":"capability://data.processing.analysis.codebase.preprocessing.integration.with.repomix","name":"codebase preprocessing integration with repomix","description":"Integrates with external codebase preprocessing tools like repomix to convert a full repository into a single text file suitable for AI analysis. DeepView expects this preprocessed file as input rather than directly indexing the repository, allowing users to control what code is included, how it's formatted, and what metadata is preserved. This separation of concerns enables flexible codebase preparation workflows while keeping the server focused on analysis.","intents":["Convert my entire repository into a format suitable for Gemini analysis","Filter out sensitive files, dependencies, or generated code before analysis","Customize how my codebase is represented (file structure, formatting, metadata)"],"best_for":["Teams wanting to control what code is exposed to AI models (security/privacy)","Developers with large repositories needing to filter before analysis","Projects with generated code or dependencies that should be excluded from analysis"],"limitations":["Requires separate preprocessing step; not automatic or real-time","No built-in filtering or customization; users must configure repomix or similar tool separately","Preprocessing must be repeated manually for code changes; no incremental updates","DeepView has no visibility into preprocessing configuration; changes to repomix settings require manual regeneration"],"requires":["External preprocessing tool (repomix or equivalent)","Repository or codebase to preprocess","Configuration for preprocessing tool (file filters, format options, etc.)"],"input_types":["repository or codebase directory","preprocessing tool configuration"],"output_types":["preprocessed codebase file (text, typically .txt or .md)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_7","uri":"capability://tool.use.integration.gemini.api.integration.with.google.generativeai.sdk","name":"gemini api integration with google-generativeai sdk","description":"Integrates with Google's google-generativeai Python SDK to send constructed prompts to Gemini models and receive responses. The server uses the SDK's client initialization (with API key from environment) and model selection to create a generative model instance, then calls the generate_content method with the full-context prompt. This pattern abstracts Gemini API details behind the SDK, handling authentication, model routing, and response parsing.","intents":["Send codebase analysis queries to Gemini without managing API calls directly","Leverage Gemini's large context window and reasoning capabilities for code understanding","Handle authentication and API key management transparently"],"best_for":["Developers wanting to use Gemini for codebase analysis without writing API integration code","Teams standardizing on Gemini for AI-powered development tools"],"limitations":["Requires active Google Gemini API key with billing enabled","API calls incur costs based on token usage; no built-in cost estimation or rate limiting","No retry logic or error recovery for API failures; transient errors cause query to fail","No streaming response support; entire response must be generated before returning to user","API rate limits apply; high-volume query patterns may hit quotas"],"requires":["Google Gemini API key (set via GEMINI_API_KEY environment variable)","Active Google Cloud billing account with Gemini API enabled","google-generativeai Python package (installed as dependency)","Network connectivity to Google's API endpoints"],"input_types":["constructed prompt (string)","model name (string)"],"output_types":["Gemini response (text)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_8","uri":"capability://tool.use.integration.mcp.tool.registration.and.protocol.compliance","name":"mcp tool registration and protocol compliance","description":"Implements the Model Context Protocol specification by registering the deepview tool with the MCP server, defining its input schema, description, and handler function. The server uses the mcp Python package to expose this tool to compatible IDEs, which can then discover and invoke it through standardized MCP messages. This pattern ensures compatibility with any MCP-aware IDE without custom integration code.","intents":["Make my codebase analysis available to any MCP-compatible IDE without custom plugins","Enable IDEs to discover and invoke the deepview tool through standardized protocol","Ensure future compatibility with new IDEs that adopt the MCP standard"],"best_for":["IDE users (Cursor, Windsurf, Claude Desktop) wanting native codebase analysis","Teams standardizing on MCP for AI tool integration across development workflows","Developers building MCP-compatible tools and wanting to avoid vendor lock-in"],"limitations":["Requires IDE support for MCP; not compatible with VS Code or other non-MCP IDEs","MCP protocol overhead adds latency compared to direct API calls","Tool discovery and invocation depend on IDE implementation; behavior may vary across IDEs","No built-in tool versioning or backward compatibility; protocol changes may break older IDEs"],"requires":["mcp Python package (installed as dependency)","MCP-compatible IDE (Cursor 0.40+, Windsurf, Claude Desktop with MCP support)","Server configured to expose deepview tool via MCP protocol"],"input_types":["MCP tool invocation message (from IDE)"],"output_types":["MCP tool response message (to IDE)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepview-mcp__cap_9","uri":"capability://text.generation.language.query.response.formatting.and.delivery","name":"query response formatting and delivery","description":"Formats Gemini's raw response text into a user-friendly format and delivers it through the appropriate channel (CLI output or IDE response message). The formatting logic may include syntax highlighting hints, code block extraction, or structured response organization depending on the delivery mode. This pattern ensures responses are readable and actionable in both CLI and IDE contexts.","intents":["Get readable, well-formatted analysis responses in my IDE or terminal","Extract code snippets or structured information from Gemini's analysis","Understand complex analysis results without manual parsing"],"best_for":["Developers wanting readable analysis output without manual formatting","Teams integrating codebase analysis into workflows where response format matters"],"limitations":["Response formatting is basic; no advanced features like syntax highlighting or interactive elements","No structured response parsing; all output is plain text","IDE-specific formatting not implemented; responses may not leverage IDE capabilities (e.g., code folding)","No response caching or deduplication; identical queries generate identical responses each time"],"requires":["Gemini response (text)","Delivery context (CLI or IDE)"],"input_types":["Gemini response (text)"],"output_types":["formatted response (text, for CLI or IDE)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.8+","Google Gemini API key (GEMINI_API_KEY environment variable)","MCP-compatible IDE (Cursor 0.40+, Windsurf, or Claude Desktop with MCP support)","Preprocessed codebase file (generated via repomix or equivalent tool)","Preprocessed codebase file (text format, typically from repomix)","Sufficient RAM to hold entire codebase in memory (typically 10-50MB for most projects)","File path to preprocessed codebase passed via --codebase-file CLI argument","Valid Gemini model name (e.g., 'gemini-2.0-flash-lite', 'gemini-1.5-pro')","Active Google Gemini API key with access to selected model","CLI invocation with --model flag (e.g., deepview-mcp --model gemini-1.5-pro)"],"failure_modes":["Requires MCP-compatible IDE; not compatible with VS Code without MCP support layer","Codebase must be preprocessed into single file format (via repomix or similar) before server startup","No incremental indexing — entire codebase reloaded on server restart, not suitable for real-time file watching","Memory overhead scales linearly with codebase size; a 1M-token codebase requires ~4-8MB of RAM for storage","No incremental updates — any code changes require regenerating the preprocessed file and restarting the server","Codebases larger than Gemini's context window (1M tokens) must be manually filtered or split before preprocessing","No differential indexing or change detection; treats entire codebase as static snapshot","Model selection is static per server instance; cannot switch models mid-session without restarting","No automatic fallback if selected model is unavailable or deprecated","No cost estimation or token counting before query execution; users must track usage manually","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"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=deepview-mcp","compare_url":"https://unfragile.ai/compare?artifact=deepview-mcp"}},"signature":"5giookS5IZMnDH5AzPezQx1LzSxCn8/UZ4JXlh43AFDlUJfumlDgUBkP3Tnn/5gSF2/kTOCmVqvRh58Mf4pkBQ==","signedAt":"2026-06-16T08:16:20.617Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepview-mcp","artifact":"https://unfragile.ai/deepview-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=deepview-mcp","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"}}