{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-google-gemini-gemini-cli","slug":"mcp-google-gemini-gemini-cli","name":"gemini-cli","type":"cli","url":"https://github.com/google-gemini/gemini-cli","page_url":"https://unfragile.ai/mcp-google-gemini-gemini-cli","categories":["cli-tools"],"tags":["ai","ai-agents","cli","gemini","gemini-api","mcp-client","mcp-server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-google-gemini-gemini-cli__cap_0","uri":"capability://text.generation.language.interactive.repl.based.multi.turn.conversation.with.gemini.models","name":"interactive repl-based multi-turn conversation with gemini models","description":"Provides a terminal-based read-eval-print loop that maintains stateful conversation history with Google's Gemini API, supporting streaming responses and turn-based message processing. The system implements a UI state machine that handles input buffering, command parsing, and response rendering while managing chat compression to keep context within token limits. Streaming is handled via the Gemini API's server-sent events, with responses progressively rendered to the terminal as tokens arrive.","intents":["I want to have a natural conversation with an AI model directly in my terminal without leaving my shell","I need to maintain conversation context across multiple turns while working on a coding task","I want to see AI responses stream in real-time as they're generated"],"best_for":["developers who spend most of their time in terminal environments","teams building CLI-first workflows and automation","users who want lightweight AI access without browser overhead"],"limitations":["Chat compression may lose fine-grained context in very long conversations (>50 turns)","Streaming latency depends on network connection to Gemini API","Terminal rendering performance degrades with extremely long single responses (>10k tokens)"],"requires":["Node.js 20 or higher","Google Gemini API key or Vertex AI credentials","Terminal with ANSI color support"],"input_types":["text prompts","file references via @-syntax","slash commands"],"output_types":["streamed text responses","formatted terminal output with syntax highlighting"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_1","uri":"capability://tool.use.integration.mcp.server.integration.and.dynamic.tool.registration","name":"mcp server integration and dynamic tool registration","description":"Dynamically discovers, connects to, and manages Model Context Protocol (MCP) servers as external tool providers, allowing the Gemini agent to execute tools defined by third-party MCP servers. The system maintains a registry of available MCP servers, handles their lifecycle (startup, shutdown, reconnection), and translates tool schemas from MCP format into Gemini function-calling format. Tool execution results are streamed back through the MCP protocol and integrated into the conversation flow.","intents":["I want to extend Gemini CLI with custom tools from MCP servers without modifying the core codebase","I need to connect multiple MCP servers and have Gemini intelligently choose which tools to use","I want to manage MCP server lifecycle (start, stop, reconnect) automatically as part of the agent"],"best_for":["teams building extensible AI agent systems","developers integrating multiple tool ecosystems (e.g., database tools, API clients, custom services)","organizations standardizing on MCP for tool interoperability"],"limitations":["MCP server discovery requires manual configuration in settings; no auto-discovery mechanism","Tool schema translation may lose MCP-specific metadata not supported by Gemini function calling","Network latency between CLI and MCP servers adds per-tool-call overhead (~50-200ms)","No built-in retry logic for failed MCP server connections; requires manual restart"],"requires":["MCP servers running and accessible (local or remote)","MCP server configuration in gemini-cli settings","Node.js 20+ for the CLI itself"],"input_types":["MCP server connection strings","tool schemas in MCP format"],"output_types":["tool execution results","structured tool responses integrated into conversation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_10","uri":"capability://tool.use.integration.extension.system.with.configuration.variables","name":"extension system with configuration variables","description":"Provides a plugin architecture for extending Gemini CLI with custom functionality through extensions that can define new tools, commands, and behaviors. Extensions are configured via settings and can access configuration variables, hooks, and the core agent API. The system supports extension lifecycle management (initialization, cleanup) and allows extensions to register custom tools that are exposed to the Gemini agent.","intents":["I want to add custom tools to Gemini CLI without forking the codebase","I need to integrate domain-specific functionality (e.g., company APIs, internal tools) into the agent","I want to share reusable extensions across teams and projects"],"best_for":["teams building customized AI agent deployments","organizations with internal tools that need AI integration","developers building extension ecosystems around Gemini CLI"],"limitations":["Extensions must be written in TypeScript/JavaScript; no support for other languages","Extension API is not versioned; breaking changes to the core API can break extensions","No built-in extension marketplace or discovery mechanism; extensions must be manually configured","Extension execution is not sandboxed; malicious extensions can access the full system"],"requires":["TypeScript/JavaScript knowledge","Extension configuration in settings","Access to the Gemini CLI core API"],"input_types":["extension configuration","tool definitions"],"output_types":["registered tools","extension lifecycle events"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_11","uri":"capability://code.generation.editing.ide.integration.and.vs.code.companion","name":"ide integration and vs code companion","description":"Provides a VS Code extension (vscode-ide-companion) that integrates Gemini CLI with the IDE, allowing users to invoke the agent from within the editor and use editor context (selected code, file paths, project structure) as input to the agent. The integration supports inline code generation, refactoring suggestions, and documentation generation directly in the editor. The VS Code extension communicates with the Gemini CLI backend via a local API.","intents":["I want to use Gemini CLI without leaving my IDE","I need to generate code or refactor selected code directly in the editor","I want to use the current file and project context as input to the agent"],"best_for":["developers who spend most of their time in VS Code","teams standardizing on VS Code as the primary development environment","developers who want seamless AI integration without context switching"],"limitations":["VS Code extension only; no support for other IDEs (JetBrains, Vim, etc.)","IDE integration requires the Gemini CLI backend to be running; adds a separate process dependency","Large file context (>1MB) may slow down IDE responsiveness when passed to the agent","No built-in conflict resolution for concurrent edits from the agent and the user"],"requires":["VS Code 1.80 or higher","Gemini CLI backend running locally","VS Code extension installed from marketplace"],"input_types":["selected code in editor","current file path","project structure"],"output_types":["generated code","refactoring suggestions","inline documentation"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_12","uri":"capability://tool.use.integration.browser.agent.and.web.interaction","name":"browser agent and web interaction","description":"Implements a browser agent that can navigate websites, extract information, and interact with web pages on behalf of the user. The agent uses browser automation (likely Puppeteer or similar) to control a headless browser, take screenshots, extract text content, and fill forms. Browser interactions are exposed as tools that the Gemini agent can invoke, allowing it to research information, fill out web forms, or automate web-based tasks.","intents":["I want the AI to research information from websites and summarize findings","I need the AI to fill out web forms or interact with web applications automatically","I want to automate web scraping or data extraction tasks"],"best_for":["teams automating web-based workflows and data extraction","developers building AI agents that need to interact with web applications","users who want the agent to research information from the web"],"limitations":["Browser automation is slow (~2-5s per page load) compared to direct API calls","JavaScript-heavy websites may not render correctly in headless browser mode","Web scraping may violate website terms of service or robots.txt","No built-in handling of authentication (login) for protected websites"],"requires":["Headless browser (Chrome/Chromium) installed and accessible","Network access to target websites"],"input_types":["website URLs","interaction instructions (click, type, scroll)"],"output_types":["page screenshots","extracted text content","interaction results"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_13","uri":"capability://automation.workflow.telemetry.and.observability.with.structured.logging","name":"telemetry and observability with structured logging","description":"Implements comprehensive telemetry and observability features that track agent execution, tool calls, API usage, and performance metrics. The system logs structured events (JSON format) that can be exported to external observability platforms (e.g., Google Cloud Logging, Datadog). Telemetry includes latency measurements, token usage, tool execution results, and error tracking. Users can configure telemetry verbosity and choose which events to export.","intents":["I want to monitor agent performance and identify bottlenecks","I need to track API usage and costs across multiple agent invocations","I want to debug agent behavior by reviewing detailed execution logs"],"best_for":["teams running agents in production and needing observability","developers debugging agent behavior and performance issues","organizations tracking AI API costs and usage"],"limitations":["Structured logging adds overhead (~5-10% latency increase) to agent execution","Telemetry data can be verbose; filtering and sampling may be needed for high-volume usage","No built-in alerting or anomaly detection; requires external tools for monitoring","Sensitive data (prompts, API keys) may be logged; requires careful configuration to avoid leaks"],"requires":["Telemetry configuration in settings","External observability platform (optional, for export)"],"input_types":["agent execution events","tool call results","API responses"],"output_types":["structured JSON logs","performance metrics","usage statistics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_14","uri":"capability://memory.knowledge.session.management.and.conversation.persistence","name":"session management and conversation persistence","description":"Manages agent sessions that persist conversation history, state, and configuration across multiple invocations. Sessions are stored locally (or optionally in external storage) and can be resumed, forked, or archived. The system supports session metadata (creation time, last modified, tags) and allows filtering/searching sessions. Session management enables long-lived agent interactions where context is preserved across terminal sessions.","intents":["I want to resume a conversation with the agent from where I left off","I need to save and archive important conversations for future reference","I want to fork a session to explore alternative paths without losing the original"],"best_for":["users having long-lived interactions with the agent across multiple days/weeks","teams collaborating on agent-assisted projects and needing shared session history","developers debugging agent behavior by replaying past sessions"],"limitations":["Session storage requires disk space; large sessions (>100MB) may degrade performance","No built-in session synchronization across multiple machines; sessions are local-only","Session restoration may not work if agent configuration has changed significantly","No built-in encryption for session files; sensitive data is stored in plaintext"],"requires":["Local file system with write permissions","Session configuration in settings"],"input_types":["session ID","session metadata"],"output_types":["conversation history","session state","session metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_15","uri":"capability://automation.workflow.hooks.system.for.lifecycle.customization","name":"hooks system for lifecycle customization","description":"Provides a hooks system that allows extensions and configurations to inject custom logic at key points in the agent lifecycle (initialization, prompt generation, tool execution, response processing). Hooks are registered by extensions or configuration and are called at specific events, allowing customization without modifying core code. The system supports pre-hooks (before an action) and post-hooks (after an action) for most major operations.","intents":["I want to customize agent behavior at specific lifecycle points without forking the codebase","I need to inject custom logic before tool execution (e.g., validation, logging)","I want to process agent responses before they're displayed to the user"],"best_for":["teams building customized agent deployments with specific requirements","developers extending agent behavior through plugins","organizations with compliance requirements that need to inject custom logic"],"limitations":["Hooks add latency to agent execution; complex hooks can significantly slow down operations","Hook execution order is not guaranteed; interdependent hooks may fail","No built-in error handling for hook failures; a failing hook can crash the agent","Hook API is not versioned; breaking changes to hooks can break extensions"],"requires":["Extension or configuration that registers hooks","Understanding of hook lifecycle and execution order"],"input_types":["hook registration","hook context and parameters"],"output_types":["hook execution results","modified context or state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_2","uri":"capability://safety.moderation.security.gated.tool.execution.with.approval.workflows","name":"security-gated tool execution with approval workflows","description":"Implements a security approval system that intercepts tool calls before execution, allowing users to review and approve/deny sensitive operations like shell commands, file writes, and API calls. The system maintains approval policies (per-tool, per-pattern, or blanket approvals) and can sandbox execution environments on macOS using Security Framework policies. Approval decisions are logged and can be configured to require interactive confirmation or auto-approve trusted patterns.","intents":["I want to prevent the AI from executing dangerous commands without my explicit approval","I need to audit which tools the AI agent has executed and what changes it made","I want to set up approval rules so common safe operations auto-approve but risky ones require confirmation"],"best_for":["teams running AI agents in production environments where safety is critical","developers who want to delegate task execution to AI but maintain control over destructive operations","organizations with compliance requirements around automated system changes"],"limitations":["Approval workflows add latency to tool execution (requires user interaction or policy lookup)","Sandbox policies on macOS are restrictive and may block legitimate operations; requires careful tuning","No built-in integration with external approval systems (e.g., Slack, PagerDuty); approvals are terminal-only","Pattern-based approval rules can be bypassed by obfuscated commands or indirect execution"],"requires":["macOS (for sandboxing features) or Linux/Windows (approval workflows only)","User configuration of approval policies in settings","Interactive terminal for approval prompts"],"input_types":["tool execution requests from Gemini","user approval/denial input"],"output_types":["approval decision (allow/deny)","audit logs of executed tools"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_3","uri":"capability://tool.use.integration.shell.command.execution.with.streaming.output.capture","name":"shell command execution with streaming output capture","description":"Executes arbitrary shell commands in the user's environment and captures their output (stdout/stderr) in real-time, streaming results back to the Gemini agent for analysis and follow-up actions. The system runs commands in the user's current shell context, preserving environment variables and working directory, and can handle long-running processes with progressive output streaming. Command execution is subject to the security approval system before running.","intents":["I want the AI to run shell commands and see their output so it can react intelligently","I need the AI to execute build commands, tests, or deployment scripts and analyze the results","I want to automate multi-step workflows where each step depends on the previous command's output"],"best_for":["developers automating development workflows (builds, tests, deployments)","DevOps engineers using AI to troubleshoot infrastructure issues","teams building AI-driven CI/CD automation"],"limitations":["Commands execute in the user's shell context, so they have the same permissions and access as the user","Long-running processes (>5 minutes) may timeout or consume excessive memory if output is very verbose","Interactive commands (requiring stdin) are not supported; only non-interactive batch commands work","Output streaming adds latency compared to buffered execution; very large outputs (>1MB) may degrade performance"],"requires":["Unix-like shell (bash, zsh, sh) or Windows PowerShell","Commands must be non-interactive","Approval from security system (if enabled)"],"input_types":["shell command strings","working directory context"],"output_types":["stdout/stderr as streamed text","exit code"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_4","uri":"capability://tool.use.integration.file.system.operations.with.context.aware.file.references","name":"file system operations with context-aware file references","description":"Provides file read, write, and directory operations through a tool system that supports @-syntax for referencing files in prompts, allowing users to include file contents directly in the conversation context. The system can read entire files or specific line ranges, write new files or append to existing ones, and list directory contents. File operations are integrated into the tool execution pipeline and subject to security approval.","intents":["I want to reference a file in my prompt and have the AI read it automatically","I need the AI to modify source code files and save the changes back to disk","I want to show the AI a directory structure so it understands the project layout"],"best_for":["developers using AI for code generation and refactoring","teams automating documentation generation or file processing","users who want to keep files in sync with AI-generated content"],"limitations":["File reads are limited to text files; binary files are not supported","Large files (>1MB) may consume excessive context tokens and slow down API calls","@-syntax only works in interactive mode; non-interactive mode requires explicit file tool calls","No built-in conflict detection for concurrent file modifications from multiple agents"],"requires":["Read/write permissions on target files and directories","Approval from security system for write operations"],"input_types":["file paths","@-syntax references","line range specifications"],"output_types":["file contents as text","write confirmation","directory listings"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_5","uri":"capability://automation.workflow.non.interactive.scripting.mode.with.prompt.based.execution","name":"non-interactive scripting mode with prompt-based execution","description":"Supports running Gemini CLI in non-interactive mode via the -p flag, executing a single prompt and returning results without entering the REPL. This mode is designed for scripting and automation, where the CLI is invoked as a subprocess with a prompt and returns structured output. The system processes the prompt through the full agent pipeline (tool execution, streaming, etc.) and exits after completion, making it suitable for shell scripts and CI/CD pipelines.","intents":["I want to invoke Gemini CLI from a shell script or CI/CD pipeline with a single prompt","I need to generate code or content programmatically and capture the output","I want to use Gemini CLI as a building block in larger automation workflows"],"best_for":["DevOps engineers integrating AI into CI/CD pipelines","developers building shell scripts that need AI assistance","teams automating content generation or code synthesis"],"limitations":["No conversation history or multi-turn context; each invocation is stateless","Output is returned as plain text; no structured JSON output format available","Streaming responses are buffered and returned all at once, not progressively","Tool execution requires pre-approval or auto-approval rules; interactive approval is not possible"],"requires":["Node.js 20+","Gemini API key or Vertex AI credentials","Shell or scripting environment to invoke the CLI"],"input_types":["command-line prompt string","optional file references"],"output_types":["plain text response","exit code indicating success/failure"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_6","uri":"capability://planning.reasoning.agent.skills.and.sub.agent.delegation","name":"agent skills and sub-agent delegation","description":"Allows defining reusable agent skills that encapsulate multi-step workflows and can be invoked by the main agent or other sub-agents. Skills are defined as specialized agents with their own system prompts, tool access, and capabilities, enabling hierarchical task decomposition. The system supports agent-to-agent (A2A) communication via the A2A Server, allowing sub-agents to be spawned dynamically and managed as part of the main agent's execution flow.","intents":["I want to define specialized agents for specific tasks (e.g., code review, testing, documentation) and have the main agent delegate to them","I need to break down complex workflows into sub-tasks that can be handled by specialized agents","I want to reuse agent skills across multiple projects without duplicating logic"],"best_for":["teams building complex multi-step AI workflows","organizations with specialized teams (QA, DevOps, documentation) that need dedicated agents","developers building hierarchical agent systems with task decomposition"],"limitations":["Sub-agent communication adds latency due to A2A Server overhead (~100-500ms per delegation)","No built-in load balancing or resource limits for spawned sub-agents; can consume excessive resources","Skill definitions require manual configuration; no auto-discovery of available skills","Error handling and retry logic for failed sub-agent calls must be implemented in the main agent's system prompt"],"requires":["A2A Server running and accessible","Skill definitions configured in settings","Sub-agents must be running or auto-spawned"],"input_types":["skill invocation requests","task context and parameters"],"output_types":["skill execution results","structured task completion status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_7","uri":"capability://planning.reasoning.model.routing.and.multi.model.support","name":"model routing and multi-model support","description":"Supports routing prompts to different Gemini models (e.g., gemini-2.0-flash, gemini-1.5-pro) based on configuration, task complexity, or cost optimization. The system can be configured to use different models for different types of tasks or to fall back to alternative models if the primary model is unavailable. Model routing is configured via settings and can be overridden per-prompt or per-session.","intents":["I want to use faster, cheaper models for simple tasks and more capable models for complex reasoning","I need to fall back to alternative models if my preferred model is rate-limited or unavailable","I want to experiment with different models and compare their outputs"],"best_for":["teams optimizing for cost and latency across different task types","developers experimenting with multiple Gemini model versions","organizations with strict SLA requirements that need fallback models"],"limitations":["Model routing logic must be configured manually; no automatic model selection based on task complexity","Different models may produce inconsistent outputs for the same prompt, complicating result comparison","Fallback routing adds latency if the primary model fails; no pre-warming of fallback models","Tool schemas may differ between models, requiring compatibility testing"],"requires":["API keys for multiple Gemini models (if using different providers)","Model routing configuration in settings"],"input_types":["model selection criteria","routing rules"],"output_types":["model selection decision","response from selected model"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_8","uri":"capability://memory.knowledge.chat.compression.and.context.management","name":"chat compression and context management","description":"Automatically compresses conversation history to stay within Gemini API token limits while preserving semantic meaning. The system uses a compression algorithm that summarizes older turns and removes redundant information, allowing long conversations to continue without hitting context limits. Compression is triggered automatically when approaching token limits and can be configured with different compression strategies.","intents":["I want to have long conversations without hitting the model's context window limit","I need to preserve important context from earlier turns while removing redundant information","I want the agent to automatically manage context without me having to manually prune history"],"best_for":["users having extended conversations (>50 turns) with the agent","teams running long-lived agent sessions that need to maintain context","developers building agents that need to work within strict token budgets"],"limitations":["Compression may lose fine-grained details from earlier turns, affecting reasoning quality","Compression algorithm is not configurable; uses a fixed strategy that may not suit all use cases","Compressed context is less readable than original conversation; debugging becomes harder","Compression adds latency (~500ms-2s) when triggered, potentially blocking user interaction"],"requires":["Long conversation history (>10k tokens) to trigger compression","Gemini API token limit awareness"],"input_types":["conversation history","token count estimates"],"output_types":["compressed conversation summary","reduced token count"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-google-gemini-gemini-cli__cap_9","uri":"capability://planning.reasoning.system.prompt.generation.and.customization","name":"system prompt generation and customization","description":"Generates and manages system prompts that define the agent's behavior, capabilities, and constraints. The system prompt is constructed from multiple sources: base prompts, tool descriptions, extension configurations, and user customizations. The system can generate different prompts for different contexts (interactive vs. non-interactive, different model versions) and supports hooks for customizing prompt generation.","intents":["I want to customize the agent's behavior and personality without modifying the core code","I need to define what tools the agent can access and how it should use them","I want to add domain-specific instructions that guide the agent's reasoning"],"best_for":["teams building customized AI agents for specific domains","developers who want to tune agent behavior without code changes","organizations with specific compliance or safety requirements"],"limitations":["System prompt changes require agent restart to take effect; no hot-reloading","Large system prompts consume significant token budget, reducing available context for user prompts","Prompt injection attacks are possible if user input is not properly sanitized","No built-in versioning or A/B testing framework for prompt experiments"],"requires":["Configuration file with custom system prompt settings","Understanding of prompt engineering best practices"],"input_types":["base system prompt template","tool descriptions","custom instructions"],"output_types":["final system prompt string","token count estimate"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Node.js 20 or higher","Google Gemini API key or Vertex AI credentials","Terminal with ANSI color support","MCP servers running and accessible (local or remote)","MCP server configuration in gemini-cli settings","Node.js 20+ for the CLI itself","TypeScript/JavaScript knowledge","Extension configuration in settings","Access to the Gemini CLI core API","VS Code 1.80 or higher"],"failure_modes":["Chat compression may lose fine-grained context in very long conversations (>50 turns)","Streaming latency depends on network connection to Gemini API","Terminal rendering performance degrades with extremely long single responses (>10k tokens)","MCP server discovery requires manual configuration in settings; no auto-discovery mechanism","Tool schema translation may lose MCP-specific metadata not supported by Gemini function calling","Network latency between CLI and MCP servers adds per-tool-call overhead (~50-200ms)","No built-in retry logic for failed MCP server connections; requires manual restart","Extensions must be written in TypeScript/JavaScript; no support for other languages","Extension API is not versioned; breaking changes to the core API can break extensions","No built-in extension marketplace or discovery mechanism; extensions must be manually configured","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9103914074572446,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.491Z","last_commit":"2026-05-02T02:10:41Z"},"community":{"stars":103019,"forks":13471,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-google-gemini-gemini-cli","compare_url":"https://unfragile.ai/compare?artifact=mcp-google-gemini-gemini-cli"}},"signature":"oNDRpH0CQbqLDCe1fLSQG5jr0nI7YS6UuAgbUQ7xq7CkvdqrVoMSM9kONqXQQIKPiSXgRu1/QPyqiSfwP2gQAw==","signedAt":"2026-06-22T15:25:28.373Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-google-gemini-gemini-cli","artifact":"https://unfragile.ai/mcp-google-gemini-gemini-cli","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-google-gemini-gemini-cli","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"}}