{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-google-gemini--gemini-cli","slug":"google-gemini--gemini-cli","name":"gemini-cli","type":"agent","url":"https://geminicli.com","page_url":"https://unfragile.ai/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-google-gemini--gemini-cli__cap_0","uri":"capability://text.generation.language.interactive.repl.based.conversational.agent.with.streaming.gemini.api.integration","name":"interactive repl-based conversational agent with streaming gemini api integration","description":"Provides a terminal-based REPL that maintains multi-turn conversation state with Google's Gemini models via streaming API responses. The system implements turn-based processing with automatic context management, handling both user input buffering and incremental token streaming from the Gemini API. Uses a state machine architecture to manage conversation lifecycle, including session persistence and chat compression for context window optimization.","intents":["I want to have a natural conversation with an AI directly in my terminal without leaving my shell","I need to maintain conversation context across multiple turns while working on a development task","I want to see AI responses stream in real-time as they're generated rather than waiting for full completion"],"best_for":["Solo developers building AI-assisted workflows in terminal environments","DevOps engineers integrating AI reasoning into shell-based automation","Teams prototyping AI agents before moving to production systems"],"limitations":["Context window limited by Gemini model (typically 1M tokens) — chat compression activates automatically but may lose nuanced conversation history","Streaming responses add ~50-100ms latency per token on typical network conditions","No built-in persistence across terminal sessions — requires explicit session save/load commands","Single-threaded REPL blocks on long-running tool executions"],"requires":["Node.js 20 or higher","Valid Google Cloud API key or Vertex AI credentials","Terminal with ANSI color support (most modern terminals)","Network connectivity to Google Gemini API endpoints"],"input_types":["natural language text","file references via @-syntax","shell commands via /shell directive","structured prompts with slash commands"],"output_types":["streaming text responses","formatted code blocks with syntax highlighting","tool execution results","structured JSON for programmatic consumption"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_1","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration.and.dynamic.tool.registration","name":"mcp (model context protocol) server integration and dynamic tool registration","description":"Dynamically discovers, loads, and manages MCP servers as external tool providers, allowing the agent to extend its capabilities beyond built-in tools. The system implements a tool registry that communicates with MCP servers via stdio or HTTP transports, automatically discovering available tools and marshaling arguments/responses through the MCP protocol. Supports both local MCP servers and remote endpoints with configurable lifecycle management.","intents":["I want to extend the agent with custom tools from third-party MCP servers without modifying core code","I need to connect the agent to specialized services (databases, APIs, monitoring systems) via MCP","I want to manage multiple MCP server instances and route tool calls to the appropriate provider"],"best_for":["Teams building extensible AI agent platforms with pluggable tool ecosystems","Organizations integrating Gemini CLI with existing MCP server infrastructure","Developers creating custom tool providers for domain-specific AI workflows"],"limitations":["MCP server discovery is static at startup — adding new servers requires CLI restart","Tool argument validation relies on MCP schema definitions; malformed schemas cause silent failures","No built-in retry logic for MCP server timeouts — long-running tools may block the REPL","HTTP transport adds ~200-500ms latency per tool call vs stdio-based local servers"],"requires":["MCP server implementation compatible with stdio or HTTP transport","MCP server configuration in gemini-cli config file","Network connectivity for HTTP-based MCP servers","Proper environment variables or credentials for MCP server authentication"],"input_types":["MCP server configuration (JSON/YAML)","tool invocation requests from Gemini model","structured arguments matching MCP tool schemas"],"output_types":["tool execution results in MCP response format","error messages and tool availability metadata","structured data from MCP servers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_10","uri":"capability://memory.knowledge.chat.compression.and.context.window.optimization.with.automatic.summarization","name":"chat compression and context window optimization with automatic summarization","description":"Automatically compresses conversation history when approaching the Gemini model's context window limit by summarizing older turns and removing redundant information. The system implements a compression strategy that identifies important context (tool results, key decisions) and summarizes conversational turns, maintaining semantic meaning while reducing token count. Compression is transparent to the user and happens automatically during turn processing.","intents":["I want to have long conversations without hitting the context window limit","I need the AI to remember important decisions and tool results while forgetting verbose explanations","I want automatic context optimization without manually managing conversation history"],"best_for":["Developers working on long-running projects with extended conversations","Teams using the agent for iterative development where context accumulates","Users working with large codebases where full file context is needed"],"limitations":["Compression may lose nuanced conversational context — important details can be summarized away","Compression adds ~500ms-1s latency when triggered (requires additional API call)","Compressed context is less useful for debugging — original turn details are lost","No user control over compression strategy — automatic behavior cannot be customized"],"requires":["Conversation history with multiple turns","Gemini model with sufficient context window (1M tokens recommended)","Automatic compression enabled in configuration"],"input_types":["conversation history with multiple turns","context window usage metrics"],"output_types":["compressed conversation history","summarization metadata","token count reduction metrics"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_11","uri":"capability://tool.use.integration.extension.system.with.custom.hooks.and.configuration.variables","name":"extension system with custom hooks and configuration variables","description":"Provides an extension mechanism that allows users to define custom hooks at various points in the agent lifecycle (pre-prompt, post-response, tool-execution) and inject configuration variables. Extensions are JavaScript/TypeScript modules that can modify prompts, intercept tool calls, and customize behavior without modifying core code. The system implements a hook registry and variable interpolation system that processes extensions during initialization.","intents":["I want to customize the agent's behavior for my specific workflow without forking the codebase","I need to inject custom logic before/after specific operations (prompts, tool calls, responses)","I want to define environment-specific variables and have them automatically interpolated"],"best_for":["Teams building custom AI workflows on top of Gemini CLI","Organizations with specific compliance or customization requirements","Developers extending the agent with domain-specific logic"],"limitations":["Extensions are loaded at startup — cannot be dynamically added during execution","Hook execution is synchronous — long-running extensions block the agent","No sandboxing for extensions — malicious extensions have full system access","Extension API is not versioned — breaking changes in core may break extensions"],"requires":["JavaScript/TypeScript knowledge for extension development","Extension files in the configured extensions directory","Proper configuration in gemini-cli config file","Node.js runtime for extension execution"],"input_types":["extension module definitions","hook registration configurations","variable definitions (JSON/YAML)"],"output_types":["modified prompts from pre-prompt hooks","intercepted tool calls from tool-execution hooks","custom responses from post-response hooks"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_12","uri":"capability://search.retrieval.browser.agent.with.web.navigation.and.content.extraction","name":"browser agent with web navigation and content extraction","description":"Provides a browser automation capability that allows the agent to navigate websites, extract content, and interact with web pages. The system implements a headless browser controller (likely using Puppeteer or similar) that can be invoked as a tool, enabling the agent to research information, verify web content, and interact with web-based services. Browser sessions are managed with configurable timeouts and resource limits.","intents":["I want the AI to research information on the web and incorporate findings into our conversation","I need the agent to verify that documentation or examples on websites are still accurate","I want the agent to interact with web-based tools and services as part of task execution"],"best_for":["Developers needing real-time web information for code generation or debugging","Teams automating web-based research and content verification","Organizations integrating AI with web-based services and APIs"],"limitations":["Browser automation adds significant latency (~5-10s per page load) and memory overhead","JavaScript-heavy websites may not render correctly in headless mode","No support for interactive elements requiring mouse/keyboard input beyond basic clicks","Browser sessions are not persistent — each invocation starts a fresh browser instance"],"requires":["Chromium or Chrome browser installed on the system","Network connectivity to target websites","Sufficient system memory for browser process (typically 200-500MB per session)","Browser tool enabled in agent configuration"],"input_types":["URLs to navigate","CSS selectors for content extraction","interaction commands (click, type, scroll)"],"output_types":["extracted web page content (HTML, text)","screenshot images","interaction results"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_13","uri":"capability://automation.workflow.telemetry.and.observability.with.structured.logging.and.performance.metrics","name":"telemetry and observability with structured logging and performance metrics","description":"Collects structured telemetry data about agent execution including API call metrics, tool execution times, token usage, and error rates. The system implements a telemetry pipeline that logs events in structured format (JSON), tracks performance metrics, and can export data to external observability platforms. Telemetry is configurable and can be disabled for privacy-sensitive deployments.","intents":["I want to understand how my agent is performing and identify bottlenecks","I need to track API costs by monitoring token usage and model invocations","I want to debug issues by reviewing structured logs of agent execution"],"best_for":["Teams operating AI agents in production with performance monitoring requirements","Organizations tracking AI costs and usage metrics","Developers debugging complex agent behaviors through structured logs"],"limitations":["Telemetry collection adds ~5-10% overhead to agent execution","Structured logs can become very large for long conversations — requires log rotation","No built-in data retention policies — logs must be manually cleaned up","Sensitive information (prompts, responses) may be logged — requires careful configuration"],"requires":["Telemetry enabled in configuration","Writable file system for log storage","Optional external observability platform credentials (Datadog, New Relic, etc.)","Log rotation/retention policies for long-term operation"],"input_types":["agent execution events","API call metrics","tool execution results"],"output_types":["structured JSON logs","performance metrics (latency, token count, error rates)","cost tracking data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_14","uri":"capability://tool.use.integration.a2a.agent.to.agent.server.protocol.for.remote.agent.communication","name":"a2a (agent-to-agent) server protocol for remote agent communication","description":"Implements a server protocol that allows Gemini CLI agents to communicate with other agents via HTTP/gRPC, enabling distributed agent systems and agent-to-agent delegation. The system provides an A2A server that exposes agent capabilities as remote endpoints, allowing other agents to invoke tools and request assistance. Uses a standardized protocol for agent discovery, capability advertisement, and request/response handling.","intents":["I want to build a distributed system of specialized agents that can communicate with each other","I need one agent to delegate complex tasks to other agents and incorporate their results","I want to expose my agent's capabilities as a service for other agents to consume"],"best_for":["Organizations building large-scale distributed agent systems","Teams with multiple specialized agents that need to collaborate","Developers creating agent networks for complex problem-solving"],"limitations":["A2A communication adds network latency (~100-500ms per agent call)","No built-in authentication/authorization — requires external security layer","Agent discovery is manual — no automatic service registration","Distributed tracing is complex — difficult to debug multi-agent workflows"],"requires":["A2A server running and accessible on the network","Agent configuration with A2A endpoint information","Network connectivity between agents","Proper firewall rules for agent-to-agent communication"],"input_types":["agent capability requests","tool invocation requests from remote agents","agent discovery queries"],"output_types":["agent capability metadata","tool execution results","agent status information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_2","uri":"capability://safety.moderation.security.gated.tool.execution.with.approval.workflows.and.sandbox.isolation","name":"security-gated tool execution with approval workflows and sandbox isolation","description":"Implements a multi-layered security system that gates tool execution through approval workflows, sandboxing, and permission policies. The system evaluates tool calls against security rules before execution, can require user approval for sensitive operations, and isolates shell command execution in macOS sandbox environments with configurable permission levels (restrictive, permissive, open). Uses a security approval system that intercepts tool calls and enforces policies based on tool type and operation.","intents":["I want to prevent the AI agent from executing dangerous shell commands without my explicit approval","I need to restrict file system access to specific directories when running untrusted agent operations","I want to audit which tools the agent executed and what permissions were granted"],"best_for":["Security-conscious teams deploying AI agents in production environments","Organizations with compliance requirements for tool execution logging and approval","Developers running AI agents on shared systems where isolation is critical"],"limitations":["Sandbox isolation only available on macOS — Linux and Windows use permission-based gating without process isolation","Approval workflows require synchronous user interaction — cannot be fully automated in non-interactive mode","Sandbox policies are static at startup — cannot dynamically adjust permissions during execution","Complex sandbox rules may cause legitimate operations to fail silently"],"requires":["macOS 10.15+ for sandbox isolation (other platforms use permission-based gating)","User interaction capability for approval workflows in interactive mode","Configuration of security policies in gemini-cli config","Proper file system permissions for the CLI process"],"input_types":["tool execution requests from Gemini model","security policy configuration (JSON/YAML)","user approval responses (yes/no/always)"],"output_types":["approval decisions (granted/denied)","audit logs of executed tools","sandbox violation errors","tool execution results"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_3","uri":"capability://memory.knowledge.file.aware.context.injection.via.syntax.file.references","name":"file-aware context injection via @-syntax file references","description":"Allows users to reference local files in prompts using @-syntax (e.g., @./src/main.ts), which automatically reads and injects file contents into the conversation context. The system implements a file resolver that parses @-references, validates file paths, reads file contents, and includes them in the prompt sent to Gemini. Supports glob patterns and directory references for batch file inclusion, with automatic syntax highlighting detection based on file extensions.","intents":["I want to ask the AI about specific code files without manually copying and pasting their contents","I need to provide multiple files as context for code review or debugging tasks","I want to reference files using relative paths from my current working directory"],"best_for":["Developers using the CLI for code analysis and debugging workflows","Teams doing code reviews with AI assistance where file context is essential","Solo developers iterating on code with AI pair-programming assistance"],"limitations":["File size limits apply — very large files (>100KB) may exceed context window when combined with other context","Binary files are not supported — only text-based files can be injected","Glob patterns are resolved at parse time — dynamic file discovery not supported","No automatic dependency resolution — must manually reference all required files"],"requires":["Files must exist and be readable by the CLI process","Proper file permissions for the user running the CLI","Valid relative or absolute file paths","Text-based file formats (code, config, markdown, etc.)"],"input_types":["natural language prompts with @-file references","relative or absolute file paths","glob patterns for multiple files"],"output_types":["file contents injected into prompt context","AI responses referencing specific file sections","syntax-highlighted code in responses"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_4","uri":"capability://tool.use.integration.shell.command.execution.with.output.capture.and.streaming","name":"shell command execution with output capture and streaming","description":"Provides a /shell command that executes arbitrary shell commands in the user's environment and captures their output for inclusion in the conversation. The system spawns a child process, streams stdout/stderr back to the REPL, and includes the command output in the next Gemini API call. Supports both interactive shell sessions and non-interactive command execution with configurable working directories and environment variables.","intents":["I want the AI to execute shell commands and see the results in the same conversation","I need to run build commands, tests, or deployment scripts and have the AI analyze the output","I want to give the AI access to system information (git status, file listings, etc.) for context"],"best_for":["DevOps engineers using AI for infrastructure automation and troubleshooting","Developers debugging build failures or test failures with AI assistance","Teams automating development workflows with AI-driven shell command execution"],"limitations":["Shell command execution is not sandboxed on Linux/Windows — requires explicit user approval for security","Long-running commands (>30s) may timeout and disconnect from the REPL","Interactive shell sessions (requiring stdin) are not supported — only non-interactive commands","Output is limited to ~100KB per command — very large outputs are truncated"],"requires":["Shell environment available on the system (bash, zsh, sh, etc.)","Proper permissions to execute commands in the target directory","Network connectivity if commands require external resources","User approval for sensitive commands (file deletion, system changes, etc.)"],"input_types":["shell command strings","working directory path","environment variables"],"output_types":["stdout/stderr from executed commands","exit codes","command execution errors"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_5","uri":"capability://automation.workflow.non.interactive.prompt.execution.with.piped.input.and.output.redirection","name":"non-interactive prompt execution with piped input and output redirection","description":"Supports non-interactive mode via the -p flag, allowing users to pipe prompts via stdin and capture AI responses via stdout. The system reads the entire prompt from stdin, sends it to Gemini, and streams the response to stdout without entering the REPL. Enables integration with shell scripts, CI/CD pipelines, and command-line tool chains where interactive mode is not feasible.","intents":["I want to use the AI agent in shell scripts and pipelines without interactive prompts","I need to integrate Gemini CLI into CI/CD workflows for automated code review or analysis","I want to pipe data through the AI agent as part of a larger command chain"],"best_for":["DevOps engineers integrating AI into CI/CD pipelines","Developers building shell scripts that leverage AI capabilities","Teams automating code analysis and documentation generation"],"limitations":["No multi-turn conversation support — each invocation is a single prompt/response pair","No interactive approval workflows — all tool executions must be pre-approved or disabled","No session persistence — conversation history is not maintained across invocations","Tool execution is limited to safe, pre-approved tools only"],"requires":["Valid stdin input containing the prompt","Gemini API credentials configured","Non-interactive mode flag (-p) in command invocation","Proper shell redirection for input/output"],"input_types":["text prompts via stdin","piped data from other commands","file contents via input redirection"],"output_types":["AI response text via stdout","errors via stderr","exit codes for success/failure"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_6","uri":"capability://planning.reasoning.model.routing.and.multi.provider.llm.selection.with.local.fallback","name":"model routing and multi-provider llm selection with local fallback","description":"Provides configurable model routing that allows users to select between Gemini API, Vertex AI, and local models (via Ollama or similar). The system maintains a model registry with provider-specific configurations, supports dynamic model switching during conversations, and implements fallback logic when primary models are unavailable. Uses a provider abstraction layer that normalizes API calls across different LLM providers.","intents":["I want to switch between different Gemini models (Pro, Flash, etc.) based on task complexity","I need to use local models for privacy-sensitive work while keeping cloud models for complex tasks","I want to configure fallback models in case my primary provider is unavailable"],"best_for":["Teams with multi-cloud or hybrid LLM strategies","Organizations with privacy requirements that mandate local model execution","Developers optimizing cost by routing simple tasks to cheaper models"],"limitations":["Model switching requires manual configuration — no automatic cost/performance optimization","Local model support depends on Ollama or compatible runtime — requires separate installation","API differences between providers may cause inconsistent tool calling behavior","Fallback logic is sequential — no parallel provider attempts for faster failover"],"requires":["Gemini API key for cloud models","Vertex AI credentials for Vertex AI models","Ollama installation and running service for local models","Model configuration in gemini-cli config file"],"input_types":["model selection commands","provider configuration (JSON/YAML)","model-specific parameters"],"output_types":["model responses","provider metadata","fallback notifications"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_7","uri":"capability://planning.reasoning.agent.skills.and.sub.agent.delegation.with.hierarchical.task.decomposition","name":"agent skills and sub-agent delegation with hierarchical task decomposition","description":"Allows definition of reusable agent skills and sub-agents that can be invoked by the main agent for specialized task execution. The system implements a skill registry where each skill is a pre-configured agent with specific instructions, tools, and capabilities. Sub-agents can be invoked via tool calls, enabling hierarchical task decomposition where complex problems are delegated to specialized agents.","intents":["I want to create specialized agents for specific domains (code review, documentation, testing) and have the main agent delegate tasks","I need to reuse common agent configurations across multiple conversations","I want to build hierarchical agent systems where complex tasks are decomposed into sub-agent calls"],"best_for":["Teams building complex AI agent systems with specialized sub-agents","Organizations with domain-specific tasks that benefit from dedicated agents","Developers creating reusable agent templates for common workflows"],"limitations":["Sub-agent invocation adds latency — each delegation requires a new API call to Gemini","No built-in context sharing between sub-agents — must explicitly pass context via tool arguments","Skill definitions are static at startup — cannot dynamically create new skills during execution","Error handling in sub-agents may not propagate clearly to the parent agent"],"requires":["Skill definitions in gemini-cli configuration","Sub-agent configuration with specific instructions and tools","Gemini API credentials for each sub-agent invocation","Clear skill naming and documentation for agent discovery"],"input_types":["skill definitions (JSON/YAML)","sub-agent configuration","task descriptions for delegation"],"output_types":["sub-agent execution results","structured task completion status","error messages from sub-agents"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_8","uri":"capability://memory.knowledge.session.management.with.conversation.history.persistence.and.resumption","name":"session management with conversation history persistence and resumption","description":"Manages conversation sessions with automatic persistence to disk, allowing users to save, load, and resume conversations across terminal sessions. The system stores conversation history, tool execution results, and session metadata in a structured format, implements session listing and search capabilities, and supports session export in multiple formats. Uses a session store abstraction that can be backed by local files or external storage.","intents":["I want to save my conversation with the AI and resume it later without losing context","I need to search through past conversations to find previous solutions or discussions","I want to export conversations for documentation, sharing, or audit purposes"],"best_for":["Developers working on long-running projects that span multiple sessions","Teams maintaining institutional knowledge through conversation archives","Organizations with audit requirements for AI agent interactions"],"limitations":["Session files can become large (>10MB) for long conversations — impacts load time","No built-in encryption — sensitive information in sessions is stored in plaintext","Session search is linear — no indexing for fast retrieval in large session archives","Session resumption may fail if referenced tools or models are no longer available"],"requires":["Writable file system for session storage","Sufficient disk space for conversation history","Session ID or name for resumption","Access to session files for export/sharing"],"input_types":["session save/load commands","session search queries","export format specifications"],"output_types":["session metadata (creation time, model, tools used)","conversation history with turn-by-turn details","exported session files (JSON, markdown, etc.)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-google-gemini--gemini-cli__cap_9","uri":"capability://code.generation.editing.ide.integration.via.vs.code.companion.extension.with.real.time.sync","name":"ide integration via vs code companion extension with real-time sync","description":"Provides a VS Code extension that integrates Gemini CLI capabilities directly into the editor, enabling inline code generation, refactoring suggestions, and conversational assistance without leaving the IDE. The system implements a bidirectional sync between the editor and CLI, allowing code selections to be sent to the agent and responses to be inserted back into the editor. Uses the VS Code extension API for editor integration and a local communication protocol for CLI sync.","intents":["I want to use the AI agent without switching away from my code editor","I need to select code in the editor and ask the AI for refactoring or explanation suggestions","I want AI-generated code to be inserted directly into my editor with proper formatting"],"best_for":["Developers who spend most of their time in VS Code","Teams standardizing on VS Code as their primary development environment","Solo developers wanting seamless AI assistance without context switching"],"limitations":["VS Code extension only — no support for other editors (Vim, Emacs, JetBrains IDEs)","Bidirectional sync adds ~100-200ms latency per operation","Large file selections (>50KB) may timeout or cause performance issues","Extension requires VS Code 1.80+ — older versions not supported"],"requires":["VS Code 1.80 or higher","Gemini CLI installed and configured on the system","VS Code extension installed from marketplace","Local communication channel between extension and CLI"],"input_types":["code selections from editor","editor commands (refactor, explain, generate)","cursor position and file context"],"output_types":["AI suggestions displayed in editor UI","code insertions at cursor position","inline diagnostics and annotations"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Node.js 20 or higher","Valid Google Cloud API key or Vertex AI credentials","Terminal with ANSI color support (most modern terminals)","Network connectivity to Google Gemini API endpoints","MCP server implementation compatible with stdio or HTTP transport","MCP server configuration in gemini-cli config file","Network connectivity for HTTP-based MCP servers","Proper environment variables or credentials for MCP server authentication","Conversation history with multiple turns","Gemini model with sufficient context window (1M tokens recommended)"],"failure_modes":["Context window limited by Gemini model (typically 1M tokens) — chat compression activates automatically but may lose nuanced conversation history","Streaming responses add ~50-100ms latency per token on typical network conditions","No built-in persistence across terminal sessions — requires explicit session save/load commands","Single-threaded REPL blocks on long-running tool executions","MCP server discovery is static at startup — adding new servers requires CLI restart","Tool argument validation relies on MCP schema definitions; malformed schemas cause silent failures","No built-in retry logic for MCP server timeouts — long-running tools may block the REPL","HTTP transport adds ~200-500ms latency per tool call vs stdio-based local servers","Compression may lose nuanced conversational context — important details can be summarized away","Compression adds ~500ms-1s latency when triggered (requires additional API call)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9103876171967449,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-02T02:10:41Z"},"community":{"stars":103017,"forks":13470,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=google-gemini--gemini-cli","compare_url":"https://unfragile.ai/compare?artifact=google-gemini--gemini-cli"}},"signature":"IOoqOjzTYUO+iuwhNPmPORlVDcA70NvkBC4L98waI+W79/GJAqf4CG2l5rB5ucckICwIsVyJPkytTymIWWH/Dg==","signedAt":"2026-06-20T18:33:13.374Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini--gemini-cli","artifact":"https://unfragile.ai/google-gemini--gemini-cli","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}