{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-oraios-serena","slug":"mcp-oraios-serena","name":"serena","type":"mcp","url":"https://github.com/oraios/serena","page_url":"https://unfragile.ai/mcp-oraios-serena","categories":["mcp-servers","rag-knowledge","code-editors"],"tags":["agent","ai","ai-coding","claude","claude-code","codex","ide","jetbrains","language-server","mcp-server","programming","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-oraios-serena__cap_0","uri":"capability://search.retrieval.symbol.level.code.navigation.and.discovery","name":"symbol-level code navigation and discovery","description":"Enables precise location and retrieval of code symbols (classes, functions, methods, variables) across a codebase by leveraging Language Server Protocol (LSP) implementations or JetBrains IDE backends for semantic understanding. Uses a SolidLanguageServer abstraction layer that normalizes symbol queries across 40+ language servers, returning structured symbol metadata including location, type, and scope without full-text search overhead.","intents":["Find all definitions of a specific function or class across the codebase","Locate where a particular symbol is used or referenced","Understand the scope and type information of a code element","Navigate between related symbols in a multi-file project"],"best_for":["AI agents performing codebase-aware refactoring","LLM clients needing precise code location for context injection","Teams building semantic code analysis tools"],"limitations":["Language server initialization adds 2-5 second startup latency per language","Symbol discovery limited to languages with available LSP implementations (40+ supported, but not all languages covered)","JetBrains backend requires IDE installation and plugin activation; LSP backend requires language-specific server binaries in PATH"],"requires":["Python 3.9+","uv package manager (astral.sh/uv)","Language server binary for target language (auto-downloaded for most, manual PATH setup for Go/Rust/etc)","Project initialized with 'serena init' or 'serena init -b JetBrains'"],"input_types":["symbol name (string)","file path (string)","line/column position (integers)"],"output_types":["structured symbol metadata (JSON with location, type, scope, definition)","list of symbol references with file paths and positions"],"categories":["search-retrieval","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_1","uri":"capability://code.generation.editing.semantic.code.editing.with.symbol.replacement","name":"semantic code editing with symbol replacement","description":"Performs targeted code modifications at the symbol level by replacing function/method bodies, renaming symbols across all references, and editing code while maintaining syntactic correctness. Operates through LSP-backed code actions and JetBrains refactoring APIs, ensuring edits respect scope and type information rather than naive text replacement.","intents":["Replace the implementation of a function while keeping its signature","Rename a symbol and automatically update all references across files","Modify method bodies in a class hierarchy with proper scope handling","Apply refactorings that require understanding code structure, not just text"],"best_for":["AI agents performing automated refactoring tasks","LLM-driven code generation that needs to integrate into existing codebases","Teams automating large-scale code transformations"],"limitations":["Edits are applied to in-memory buffers; requires explicit file write operations to persist changes","Complex refactorings (e.g., moving symbols between files) may require multi-step operations","JetBrains backend limited to languages supported by the IDE; LSP backend limited to languages with refactoring-capable servers","No built-in conflict resolution for concurrent edits to the same symbol"],"requires":["Python 3.9+","Active language server or JetBrains IDE connection","Target file must be indexed and loaded in the language server's buffer","Write permissions on target files"],"input_types":["symbol name (string)","new code body (string)","file path (string)","new symbol name for rename operations (string)"],"output_types":["modified code (string)","list of affected files and line ranges (structured data)","success/failure status with error details"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_10","uri":"capability://planning.reasoning.task.execution.system.with.agent.orchestration","name":"task execution system with agent orchestration","description":"Provides a task execution framework (SerenaAgent core) that orchestrates multi-step code operations, manages tool invocation sequences, and tracks task state across multiple tool calls. Enables agents to decompose complex refactoring or code generation tasks into sequences of symbol lookups, edits, and validations, with error handling and rollback capabilities.","intents":["Execute multi-step refactoring tasks (find symbol, check references, rename, update callers)","Orchestrate code generation workflows (find insertion point, generate code, format, validate)","Manage task state across multiple tool invocations without losing context","Handle errors and rollback partial changes if a task fails mid-execution"],"best_for":["AI agents performing complex multi-step code transformations","Teams building automated refactoring pipelines","Developers who need task-level error handling and rollback"],"limitations":["Task state is in-memory; no persistence across server restarts","Rollback capability limited to in-memory buffers; file system changes not automatically reverted","No built-in task scheduling or retry logic; failed tasks require manual intervention","Task execution is synchronous; no parallel task execution or async/await support","No audit trail or task history; completed tasks not logged for debugging"],"requires":["Python 3.9+","Serena initialized with language server or JetBrains backend","MCP client capable of invoking multiple tools in sequence"],"input_types":["task definition (sequence of tool invocations)","tool parameters (symbol names, file paths, code snippets)"],"output_types":["task execution results (modified code, affected files)","task state and progress (JSON)","error messages and rollback status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_11","uri":"capability://automation.workflow.language.server.lifecycle.management.and.buffer.synchronization","name":"language server lifecycle management and buffer synchronization","description":"Manages the full lifecycle of language servers (initialization, shutdown, capability negotiation) and maintains synchronized code buffers across servers as files are edited. Handles LSP protocol state machine, tracks open/closed documents, and ensures language servers have current code state for accurate analysis and refactoring.","intents":["Automatically initialize language servers on first use without manual configuration","Keep language server buffers synchronized with file edits","Gracefully shutdown language servers to free resources","Handle language server crashes and reconnect automatically"],"best_for":["Agents working with multiple language servers simultaneously","Long-running Serena instances that need resource management","Teams that want automatic language server setup without manual configuration"],"limitations":["Language server initialization adds 2-5 second startup latency per language","Buffer synchronization overhead (~50-100ms per file change) can accumulate with large projects","Language server crashes not automatically detected; may require manual restart","No built-in health checks or monitoring for language server status","Memory leaks in language servers not managed by Serena; may require periodic restarts"],"requires":["Python 3.9+","Language server binary for target language (auto-downloaded for most)","Sufficient system resources (RAM, CPU) for language server processes"],"input_types":["file path (string)","language identifier (string)","file content (string, for buffer updates)"],"output_types":["language server initialization status (JSON)","buffer synchronization status","language server capabilities (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_12","uri":"capability://memory.knowledge.caching.system.for.symbol.indexes.and.file.metadata","name":"caching system for symbol indexes and file metadata","description":"Implements multi-level caching (file metadata, symbol indexes, language server responses) to avoid redundant analysis and improve query performance. Caches symbol definitions, references, and type information from language servers, with cache invalidation triggered by file changes detected through buffer synchronization.","intents":["Speed up repeated symbol queries by caching language server responses","Reduce language server load by avoiding redundant analysis","Improve agent responsiveness by serving cached results for unchanged files","Track which files have changed since last analysis"],"best_for":["Long-running Serena instances with repeated queries on the same codebase","Large projects where language server analysis is expensive","Agents that perform multiple passes over the same code"],"limitations":["Cache invalidation is file-based; cross-file dependencies may cause stale cache entries","Cache memory usage scales with project size; large projects may consume significant RAM","No persistent cache; cache cleared on server restart","Cache coherency issues if files are modified outside Serena (e.g., by git operations)","No cache statistics or monitoring; difficult to debug cache-related performance issues"],"requires":["Python 3.9+","Sufficient RAM for cache storage","File change detection via buffer synchronization"],"input_types":["file path (string)","symbol query (name, position, scope)"],"output_types":["cached symbol metadata (JSON)","cache hit/miss status","cache statistics (optional)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_2","uri":"capability://tool.use.integration.mcp.server.exposure.of.ide.like.tools","name":"mcp server exposure of ide-like tools","description":"Wraps Serena's code analysis and editing capabilities as a Model Context Protocol (MCP) server, exposing symbol-level tools (FindSymbolTool, FindReferencingSymbolsTool, ReplaceSymbolBodyTool, RenameSymbolTool) that LLM clients can invoke during reasoning loops. Supports both stdio (client-managed lifecycle) and streamable-HTTP (user-managed, shared access) transport modes, with context-aware tool filtering based on client type (Claude Code, Cursor, VSCode, terminal agents).","intents":["Integrate Serena's code tools into Claude Code, Cursor, or other MCP-compatible clients","Expose semantic code capabilities to LLM agents running in terminal or headless environments","Share a single Serena instance across multiple LLM clients via HTTP transport","Customize tool availability and system prompts based on client context (IDE vs agent vs desktop app)"],"best_for":["Teams using Claude Code, Cursor, or VSCode with MCP support","Developers building custom LLM agents that need code understanding","Organizations deploying shared Serena instances for multiple users/clients"],"limitations":["stdio transport requires client to manage server lifecycle; not suitable for long-running shared instances","streamable-HTTP transport adds network latency (~50-200ms per tool call) compared to local LSP","Tool availability depends on context configuration; not all tools exposed in all contexts (e.g., 'desktop-app' context may disable certain tools)","No built-in authentication or rate limiting; HTTP transport requires external security layer for multi-user deployments"],"requires":["Python 3.9+","MCP-compatible client (Claude Desktop, Cursor, VSCode with MCP extension, or custom agent)","Serena initialized with 'serena init' or 'serena init -b JetBrains'","For HTTP transport: port availability and network access between client and server"],"input_types":["MCP tool invocations (JSON-RPC 2.0 format)","tool parameters (symbol names, file paths, code snippets)"],"output_types":["MCP tool results (JSON-RPC responses)","structured code metadata, modified code, or error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_3","uri":"capability://memory.knowledge.language.agnostic.semantic.code.analysis.via.lsp.abstraction","name":"language-agnostic semantic code analysis via lsp abstraction","description":"Abstracts Language Server Protocol (LSP) differences across 40+ language servers (Python, JavaScript, Go, Rust, Java, C++, etc.) through a unified SolidLanguageServer framework, enabling agents to perform semantic analysis without language-specific logic. Manages language server lifecycle (initialization, shutdown, buffer synchronization), handles LSP protocol nuances, and normalizes responses into a consistent symbol metadata format.","intents":["Analyze code semantics across polyglot projects without custom per-language implementations","Automatically select and initialize the appropriate language server for a given file","Maintain synchronized code buffers across language servers as files are edited","Query symbol information, type hints, and references in a language-agnostic way"],"best_for":["Agents working on polyglot codebases (Python + JavaScript + Go, etc.)","Teams building language-agnostic code analysis tools","Developers who want LSP capabilities without managing individual language server configurations"],"limitations":["Language server quality and feature completeness varies; some languages have immature LSP implementations","Initialization latency per language server (2-5 seconds typical); not suitable for real-time interactive use","Some language servers require manual PATH setup (Go's gopls, Rust's rust-analyzer); others auto-download","LSP protocol version mismatches between Serena and language servers can cause feature gaps or errors","Memory overhead scales with number of active language servers and project size"],"requires":["Python 3.9+","Language server binary for each target language (auto-downloaded for most; manual setup for Go, Rust, C++, etc.)","Project files indexed and loaded into language server buffers","Sufficient disk space for language server caches and indexes"],"input_types":["file path (string)","language identifier (string, e.g., 'python', 'javascript')","symbol queries (name, position, scope)"],"output_types":["normalized symbol metadata (JSON with type, location, scope, documentation)","language-agnostic code analysis results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_4","uri":"capability://tool.use.integration.jetbrains.ide.backend.integration.for.semantic.code.operations","name":"jetbrains ide backend integration for semantic code operations","description":"Provides an alternative to LSP by integrating directly with JetBrains IDEs (IntelliJ, PyCharm, GoLand, etc.) through a plugin interface, leveraging the IDE's built-in semantic analysis engine for code navigation, refactoring, and symbol resolution. Communicates with the IDE via LSP protocol handler, allowing agents to use JetBrains' advanced refactoring capabilities and type inference without managing separate language servers.","intents":["Use JetBrains IDE's semantic analysis for code operations when LSP is unavailable or insufficient","Leverage JetBrains' advanced refactoring tools (safe rename, move, extract method) in agent workflows","Integrate with existing JetBrains IDE setups without installing additional language servers","Access JetBrains' superior type inference and code completion data for code generation"],"best_for":["Teams already using JetBrains IDEs (IntelliJ, PyCharm, GoLand, WebStorm, etc.)","Projects requiring advanced refactoring capabilities beyond standard LSP","Developers who want to avoid language server setup and maintenance"],"limitations":["Requires JetBrains IDE installation and active plugin; not suitable for headless/server-only deployments","IDE must be running and project must be open; adds IDE memory and CPU overhead","Limited to languages supported by the specific JetBrains IDE (e.g., PyCharm for Python, IntelliJ for Java)","Plugin communication latency (~100-500ms per operation) higher than local LSP","IDE plugin development and maintenance burden; updates tied to IDE release cycles"],"requires":["JetBrains IDE (IntelliJ IDEA, PyCharm, GoLand, WebStorm, etc.) installed and running","Serena JetBrains plugin installed in the IDE","Project open in the IDE with indexing complete","IDE version compatibility with Serena plugin"],"input_types":["file path (string)","symbol name (string)","refactoring parameters (new name, target location, etc.)"],"output_types":["refactoring results (modified code, affected files)","symbol metadata from IDE's semantic model","IDE-generated code completion and type information"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_5","uri":"capability://automation.workflow.context.aware.tool.filtering.and.system.prompt.composition","name":"context-aware tool filtering and system prompt composition","description":"Dynamically configures which tools are exposed and how the system prompt is composed based on the client context (claude-code, ide, codex, agent, desktop-app, etc.), allowing a single Serena instance to adapt its behavior for different LLM clients. Context configuration files define tool availability, system prompt templates, and behavioral parameters, enabling agents to receive optimized tool surfaces without requiring separate server instances.","intents":["Expose different tool sets to Claude Code vs terminal agents vs IDE plugins","Customize system prompts based on client capabilities and use case","Optimize tool availability for specific workflows (e.g., 'agent' context enables all tools; 'desktop-app' context disables dangerous operations)","Support multiple client types from a single Serena deployment"],"best_for":["Teams deploying Serena to multiple client types (Claude Code, Cursor, terminal agents, IDE plugins)","Organizations that want to customize tool availability per client without running separate instances","Developers building context-specific agent workflows"],"limitations":["Context configuration is static; runtime context switching requires server restart","No per-user or per-project context isolation; context is global to the server instance","Custom context definitions require YAML editing; no UI for context management","Context selection via --context flag at server startup; not dynamically negotiated with client"],"requires":["Serena MCP server running with --context flag","Context configuration files (built-in contexts: claude-code, ide, codex, agent, desktop-app, etc.)","MCP client that supports context-aware tool discovery"],"input_types":["context name (string, e.g., 'claude-code', 'agent')","context configuration (YAML)"],"output_types":["filtered tool list (JSON)","composed system prompt (string)","context-specific behavioral parameters"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_6","uri":"capability://memory.knowledge.project.scoped.codebase.indexing.and.file.buffer.management","name":"project-scoped codebase indexing and file buffer management","description":"Maintains an in-memory representation of a project's file structure and code buffers, tracking file changes, managing ignore patterns (via .gitignore and .serenaignore), and synchronizing buffers with language servers. Implements a file buffer abstraction that allows agents to edit code in memory before persistence, and provides efficient caching of file metadata and symbol indexes.","intents":["Track which files have been modified in the current session without persisting to disk","Respect project-specific ignore patterns (.gitignore, .serenaignore) when indexing","Maintain synchronized code buffers across language servers as files are edited","Query file metadata and structure without re-reading from disk"],"best_for":["Agents performing multi-file edits that need to preview changes before persistence","Projects with large codebases where re-indexing is expensive","Teams that want to exclude certain files/directories from agent analysis"],"limitations":["In-memory buffers consume RAM proportional to project size; large projects (>1GB code) may cause memory pressure","Buffer synchronization with language servers adds latency (~50-100ms per file change)","Ignore pattern matching uses Python's gitignore library; edge cases may differ from git's behavior","No built-in persistence of buffer state; agent must explicitly write files to persist changes","Concurrent edits to the same file from multiple agents can cause buffer conflicts"],"requires":["Python 3.9+","Project initialized with 'serena init'",".gitignore and/or .serenaignore files for ignore pattern configuration","Sufficient RAM for in-memory buffer storage"],"input_types":["file path (string)","file content (string)","ignore patterns (gitignore format)"],"output_types":["file metadata (size, modification time, language)","file buffer content (string)","list of indexed files (structured data)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_7","uri":"capability://tool.use.integration.multi.transport.mcp.server.with.stdio.and.http.support","name":"multi-transport mcp server with stdio and http support","description":"Exposes Serena's tools via Model Context Protocol (MCP) using two transport mechanisms: stdio (default, client-managed lifecycle) for single-client scenarios, and streamable-HTTP (--transport streamable-http --port <port>) for shared, multi-client deployments. Handles MCP JSON-RPC 2.0 protocol, tool invocation routing, and response serialization transparently.","intents":["Run Serena as an MCP server for Claude Desktop or other stdio-based MCP clients","Deploy a shared Serena instance accessible to multiple LLM clients via HTTP","Switch between stdio and HTTP transports without code changes (via CLI flags)","Integrate Serena into existing MCP-based agent architectures"],"best_for":["Teams using Claude Desktop or other stdio-based MCP clients","Organizations deploying shared Serena instances for multiple users","Developers building custom MCP-compatible agents"],"limitations":["stdio transport requires client to manage server lifecycle; not suitable for long-running shared instances","HTTP transport adds network latency (~50-200ms per tool call) and requires external security layer for multi-user access","No built-in load balancing or connection pooling for HTTP transport","MCP protocol version negotiation may fail with older clients; no backward compatibility guarantees","stdio transport limited to single client; multiple clients require HTTP transport or multiple server instances"],"requires":["Python 3.9+","MCP-compatible client (Claude Desktop, Cursor, VSCode with MCP extension, or custom agent)","For HTTP transport: port availability and network access between client and server","Serena initialized with 'serena init' or 'serena init -b JetBrains'"],"input_types":["MCP JSON-RPC 2.0 requests","tool invocation parameters (symbol names, file paths, code snippets)"],"output_types":["MCP JSON-RPC 2.0 responses","tool results (structured code metadata, modified code, error messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_8","uri":"capability://automation.workflow.configuration.hierarchy.with.project.and.global.scopes","name":"configuration hierarchy with project and global scopes","description":"Implements a three-level configuration hierarchy (global defaults, project-specific overrides, CLI flags) that allows fine-grained control over Serena behavior without modifying code. Configuration files define language server settings, tool availability, ignore patterns, and context-specific parameters, with project-level configs overriding global defaults and CLI flags overriding both.","intents":["Configure Serena globally for all projects (e.g., default language servers, ignore patterns)","Override global settings per project (e.g., custom language server paths, tool restrictions)","Pass runtime configuration via CLI flags without editing config files","Manage different configurations for different environments (development, CI/CD, production)"],"best_for":["Teams managing multiple projects with different Serena configurations","Organizations deploying Serena across different environments","Developers who want to customize Serena behavior without code changes"],"limitations":["Configuration files are YAML; no UI for configuration management","Configuration changes require server restart; no hot-reload support","No schema validation for configuration files; invalid YAML silently fails or causes cryptic errors","Configuration hierarchy can be confusing; precedence rules (global < project < CLI) not always obvious","No built-in configuration versioning or rollback mechanism"],"requires":["Python 3.9+","Configuration files in YAML format","Project initialized with 'serena init' (creates project-level config)","Global config location (typically ~/.serena/config.yml or similar)"],"input_types":["configuration files (YAML)","CLI flags (--context, --transport, --port, etc.)"],"output_types":["merged configuration (JSON or internal representation)","effective settings for current execution"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-oraios-serena__cap_9","uri":"capability://search.retrieval.semantic.code.search.and.reference.discovery","name":"semantic code search and reference discovery","description":"Locates all usages and references of a code symbol across the entire codebase using language server semantic analysis, returning structured results with file paths, line numbers, and context. Unlike text-based search (grep), semantic search understands code structure and avoids false positives from comments, strings, or unrelated identifiers with the same name.","intents":["Find all places where a function or class is called or referenced","Identify dead code by checking if a symbol has any references","Understand the impact of renaming or removing a symbol","Trace data flow by following symbol references across files"],"best_for":["Agents performing impact analysis before refactoring","Teams identifying unused code for cleanup","Developers understanding code dependencies and call graphs"],"limitations":["Semantic search quality depends on language server implementation; some languages have incomplete reference tracking","Dynamic references (reflection, eval, metaprogramming) not detected by static analysis","Large codebases may return thousands of results; no built-in pagination or filtering","Cross-language references (e.g., Python calling JavaScript via subprocess) not detected","Performance degrades with project size; large projects (>100k files) may timeout"],"requires":["Python 3.9+","Language server initialized for target language","Project indexed and loaded in language server buffers"],"input_types":["symbol name (string)","file path (string, optional for scoped search)"],"output_types":["list of references (JSON with file path, line number, column, context snippet)","reference count (integer)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","uv package manager (astral.sh/uv)","Language server binary for target language (auto-downloaded for most, manual PATH setup for Go/Rust/etc)","Project initialized with 'serena init' or 'serena init -b JetBrains'","Active language server or JetBrains IDE connection","Target file must be indexed and loaded in the language server's buffer","Write permissions on target files","Serena initialized with language server or JetBrains backend","MCP client capable of invoking multiple tools in sequence","Language server binary for target language (auto-downloaded for most)"],"failure_modes":["Language server initialization adds 2-5 second startup latency per language","Symbol discovery limited to languages with available LSP implementations (40+ supported, but not all languages covered)","JetBrains backend requires IDE installation and plugin activation; LSP backend requires language-specific server binaries in PATH","Edits are applied to in-memory buffers; requires explicit file write operations to persist changes","Complex refactorings (e.g., moving symbols between files) may require multi-step operations","JetBrains backend limited to languages supported by the IDE; LSP backend limited to languages with refactoring-capable servers","No built-in conflict resolution for concurrent edits to the same symbol","Task state is in-memory; no persistence across server restarts","Rollback capability limited to in-memory buffers; file system changes not automatically reverted","No built-in task scheduling or retry logic; failed tasks require manual intervention","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.737238430913191,"quality":0.5,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-05-02T18:56:45Z"},"community":{"stars":23763,"forks":1596,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-oraios-serena","compare_url":"https://unfragile.ai/compare?artifact=mcp-oraios-serena"}},"signature":"/6eDfwbz4/h9a7oWpRnm3ky0tRA6ULc8Z/hvPSlvvLEOL562YRUPKiWxSXcaZ1L2qzijhUPZtc0PfY/bb5vmCA==","signedAt":"2026-06-21T14:31:15.340Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-oraios-serena","artifact":"https://unfragile.ai/mcp-oraios-serena","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-oraios-serena","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"}}