{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-stacklok--toolhive","slug":"stacklok--toolhive","name":"toolhive","type":"mcp","url":"https://stacklok.com/download/","page_url":"https://unfragile.ai/stacklok--toolhive","categories":["mcp-servers"],"tags":["ai","ai-security","aicodeassistant","golang","kubernetes","mcp","mcp-security","mcp-servers","mcp-tools","model-context-protocol","security"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-stacklok--toolhive__cap_0","uri":"capability://automation.workflow.mcp.server.lifecycle.management.with.container.runtime.abstraction","name":"mcp server lifecycle management with container runtime abstraction","description":"ToolHive manages the complete lifecycle of MCP servers (startup, shutdown, scaling, health monitoring) through a container runtime abstraction layer that supports multiple execution environments (Docker, Kubernetes, local processes). The system uses a RunConfig-based approach to define workload specifications, with middleware architecture enabling request-level policy enforcement and credential injection before tool execution. This abstraction decouples MCP server definitions from their deployment target, allowing the same server configuration to run locally during development or in Kubernetes clusters in production.","intents":["I need to run MCP servers consistently across my laptop, staging, and production Kubernetes clusters without rewriting configurations","I want to enforce security policies and inject credentials at request time before any MCP tool executes","I need to scale MCP server instances dynamically based on load while maintaining isolation"],"best_for":["Platform teams managing AI tool access across multiple deployment environments","Enterprise organizations requiring consistent MCP server orchestration from dev to production","Teams migrating from ad-hoc MCP server management to centralized governance"],"limitations":["Container runtime abstraction adds ~50-100ms overhead per workload startup due to initialization layers","Kubernetes operator requires CRD installation and RBAC configuration; not suitable for serverless/FaaS environments","Local process execution mode lacks process isolation guarantees of containerized runtimes"],"requires":["Go 1.21+ (for building from source)","Docker or Kubernetes cluster for containerized deployments","Redis instance for distributed state management in multi-node setups"],"input_types":["RunConfig YAML/JSON specifications","Container images or local executable paths","Kubernetes Custom Resource Definitions (CRDs)"],"output_types":["Running MCP server instances","Health status and metrics","Workload lifecycle events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_1","uri":"capability://search.retrieval.mcp.server.registry.with.semantic.search.and.discovery","name":"mcp server registry with semantic search and discovery","description":"ToolHive maintains a centralized registry of available MCP servers with semantic search capabilities for tool discovery. The registry stores server metadata (capabilities, schemas, permissions) and uses semantic indexing to match user requests to appropriate tools based on intent rather than exact keyword matching. The system supports both local registry operations and integration with external registries, enabling organizations to curate approved tools while preventing unauthorized tool execution through permission profiles.","intents":["I want to discover which MCP tools are available for a given task without knowing exact tool names","I need to restrict which MCP servers my team can access based on their role and project","I want to maintain a curated list of approved tools and prevent execution of unapproved servers"],"best_for":["Organizations building internal tool marketplaces for AI agents","Teams needing fine-grained access control over which MCP servers different users can invoke","Enterprises requiring audit trails of tool discovery and usage patterns"],"limitations":["Semantic search accuracy depends on quality of server metadata and schema descriptions; poor documentation reduces discoverability","Registry lookups add ~20-50ms latency per tool selection operation","No built-in versioning strategy for MCP servers; managing multiple versions requires external coordination"],"requires":["MCP server metadata with schema definitions (OpenAPI/JSON Schema format)","Vector embedding service or local embedding model for semantic search","Permission profile definitions in YAML/JSON format"],"input_types":["MCP server schemas and metadata","Natural language tool queries","Permission profile specifications"],"output_types":["Ranked list of matching MCP servers","Server capability descriptions","Access control decisions (allow/deny)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_10","uri":"capability://safety.moderation.supply.chain.security.with.image.scanning.and.attestation","name":"supply chain security with image scanning and attestation","description":"ToolHive integrates supply chain security controls for container images used by MCP servers, including image scanning for vulnerabilities and support for image attestation and signing verification. The system can validate that container images come from trusted sources and have not been tampered with before deploying them as MCP servers. This enables organizations to enforce security policies requiring only approved, scanned, and signed container images to be used for MCP server execution.","intents":["I want to ensure only vulnerability-scanned container images are used for MCP servers","I need to verify that container images are signed by trusted sources before deployment","I want to enforce supply chain security policies across all MCP server deployments"],"best_for":["Security-conscious organizations requiring container image scanning and attestation","Enterprises with strict supply chain security policies","Teams using container registries with built-in scanning capabilities"],"limitations":["Image scanning adds ~30-60 seconds to deployment time depending on image size and registry","Attestation verification requires integration with signing infrastructure (Sigstore, etc.); adds operational complexity","Scanning results are point-in-time; vulnerabilities discovered after deployment are not automatically detected"],"requires":["Container registry with image scanning capabilities","Image signing infrastructure (Sigstore, Cosign, or equivalent)","Attestation verification configuration"],"input_types":["Container image references","Signing keys and certificates","Attestation policies"],"output_types":["Scan results with vulnerability information","Attestation verification results","Deployment approval/rejection decisions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_11","uri":"capability://automation.workflow.observability.and.telemetry.with.structured.logging.and.metrics","name":"observability and telemetry with structured logging and metrics","description":"ToolHive provides comprehensive observability through structured logging of all operations, metrics collection for performance monitoring, and integration with standard observability platforms. The system logs request/response data, policy decisions, authentication events, and workload lifecycle events in structured JSON format suitable for log aggregation and analysis. Metrics are exposed in Prometheus format for integration with monitoring systems, enabling operators to track MCP server performance, request latency, error rates, and resource utilization.","intents":["I want to monitor MCP server performance and request latency in production","I need to debug issues by examining structured logs of tool invocations and policy decisions","I want to integrate ToolHive metrics with my existing monitoring and alerting infrastructure"],"best_for":["Operations teams managing production MCP server deployments","Organizations requiring detailed observability for compliance and debugging","Teams using Prometheus, ELK, or other standard observability platforms"],"limitations":["Structured logging adds ~5-10ms overhead per request due to serialization","Metrics cardinality can explode with high-dimensional labels; requires careful metric design","Log volume can be substantial in high-throughput scenarios; requires log aggregation and retention policies"],"requires":["Log aggregation system (ELK, Splunk, CloudWatch, etc.)","Prometheus-compatible metrics scraper","Appropriate log retention and storage capacity"],"input_types":["MCP server operations and events","Request/response data","Performance metrics"],"output_types":["Structured JSON logs","Prometheus metrics","Traces and spans (if tracing enabled)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_12","uri":"capability://safety.moderation.permission.profiles.for.fine.grained.access.control","name":"permission profiles for fine-grained access control","description":"ToolHive implements permission profiles that define granular access control policies mapping identities (users, applications, roles) to specific MCP servers and tools they can invoke. Permission profiles support multiple matching strategies (exact match, pattern matching, semantic matching) and can include conditions based on request context (time of day, source IP, etc.). The system evaluates permission profiles at request time, enabling dynamic access control decisions without requiring static role assignments.","intents":["I want to restrict specific users to only certain MCP tools based on their role or project","I need to define time-based access policies (e.g., only allow tool X during business hours)","I want to enforce context-aware access control based on request source, time, and other factors"],"best_for":["Organizations with complex access control requirements and multiple user roles","Teams needing context-aware authorization decisions","Enterprises requiring fine-grained audit trails of access decisions"],"limitations":["Permission profile evaluation adds ~20-50ms latency per request depending on profile complexity","Complex permission profiles with many conditions become difficult to maintain and debug","No built-in UI for permission profile management; requires manual YAML/JSON editing"],"requires":["Permission profile definitions in YAML/JSON format","Identity provider integration for user/role information","Context information available at request time (source IP, timestamp, etc.)"],"input_types":["Permission profile specifications","Identity and context information","MCP server and tool identifiers"],"output_types":["Access control decisions (allow/deny/conditional)","Audit logs of access decisions","Permission evaluation details for debugging"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_13","uri":"capability://automation.workflow.skills.system.for.extending.platform.capabilities","name":"skills system for extending platform capabilities","description":"ToolHive includes a skills system that enables extending platform capabilities through composable skill definitions. Skills are reusable components that encapsulate specific functionality (e.g., code review assistance, story implementation, PR splitting) and can be invoked through the platform. The skills system uses a declarative SKILL.md format for defining skill metadata, inputs, outputs, and implementation details. This enables platform teams to build and share custom capabilities without modifying core ToolHive code.","intents":["I want to extend ToolHive with custom capabilities specific to my organization","I need to create reusable skill components that can be shared across teams","I want to compose multiple skills to create complex workflows"],"best_for":["Platform teams building custom extensions to ToolHive","Organizations creating domain-specific skills for their workflows","Teams wanting to share and reuse skill implementations across projects"],"limitations":["Skills system documentation is limited; requires understanding of SKILL.md format and implementation patterns","No built-in skill marketplace or discovery mechanism; skills must be manually registered","Skill composition and error handling requires careful implementation; no built-in orchestration framework"],"requires":["SKILL.md format understanding and template","Implementation of skill logic (Go code or external service)","Skill registration in ToolHive configuration"],"input_types":["Skill definitions in SKILL.md format","Skill input parameters","Skill composition specifications"],"output_types":["Skill execution results","Skill metadata and documentation","Skill composition outputs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_2","uri":"capability://safety.moderation.request.level.authentication.and.authorization.with.identity.policies","name":"request-level authentication and authorization with identity policies","description":"ToolHive enforces identity and access policies at the request level through an authentication and authorization system that validates caller identity, applies organizational policies, and injects credentials into MCP server execution contexts. The system uses a middleware architecture to intercept requests before tool execution, checking permissions against defined profiles and injecting secrets from a secure secrets management system. This enables fine-grained access control where different users or applications can invoke the same MCP server with different permission levels and credential sets.","intents":["I need to ensure only authorized users can invoke specific MCP tools in my organization","I want to inject different credentials (API keys, database passwords) into MCP servers based on the caller's identity","I need to audit which user invoked which tool and when, for compliance purposes"],"best_for":["Enterprise teams requiring role-based access control (RBAC) for AI tool execution","Organizations with multi-tenant deployments where different teams need isolated tool access","Compliance-heavy industries (finance, healthcare) needing detailed audit trails of tool usage"],"limitations":["Middleware-based policy enforcement adds ~30-80ms latency per request due to policy evaluation and credential injection","Secrets management requires external storage (Redis, Kubernetes Secrets); no built-in encrypted local storage","Policy evaluation is synchronous; complex policies with many conditions can cause request timeouts"],"requires":["Identity provider integration (OIDC, OAuth2, or custom auth scheme)","Secrets storage backend (Redis, Kubernetes Secrets, or compatible system)","Permission profile definitions mapping identities to MCP server access"],"input_types":["Authentication tokens or credentials","Permission profile specifications","Identity and access policy definitions"],"output_types":["Authorization decision (allow/deny/conditional)","Injected credentials for MCP server execution","Audit log entries with caller identity and tool invoked"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_3","uri":"capability://safety.moderation.secrets.management.with.secure.credential.injection","name":"secrets management with secure credential injection","description":"ToolHive provides a secrets management system that securely stores and injects credentials into MCP server execution contexts at request time. The system integrates with external secret stores (Redis, Kubernetes Secrets) and uses a credential injection middleware to populate environment variables or configuration files for MCP servers without exposing secrets in logs or configurations. Secrets are retrieved on-demand during request processing and never persisted in workload definitions, reducing the attack surface for credential compromise.","intents":["I need to provide API keys and database credentials to MCP servers without hardcoding them in configurations","I want to rotate credentials without redeploying MCP servers or updating configurations","I need to ensure secrets are never logged or exposed in audit trails"],"best_for":["Teams managing multiple MCP servers that require different sets of credentials","Organizations with credential rotation policies requiring frequent secret updates","Security-conscious teams needing to minimize credential exposure in configurations and logs"],"limitations":["Secrets retrieval adds ~20-40ms latency per request due to external store lookups","No built-in encryption at rest; relies on external secret store security (Redis requires TLS configuration)","Secrets are decrypted in memory during MCP server execution; no protection against memory dumps or side-channel attacks"],"requires":["External secrets storage backend (Redis, Kubernetes Secrets, or compatible system)","Secret definitions with key-value pairs in YAML/JSON format","TLS/encryption configuration for secrets in transit"],"input_types":["Secret key-value pairs","Secret reference identifiers","Credential rotation policies"],"output_types":["Injected environment variables in MCP server process","Mounted secret files in containerized environments","Audit logs of secret access (without exposing secret values)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_4","uri":"capability://tool.use.integration.virtual.mcp.server.abstraction.for.tool.composition","name":"virtual mcp server abstraction for tool composition","description":"ToolHive implements a Virtual MCP Server (vMCP) abstraction that allows multiple physical MCP servers to be composed into a single logical server interface. This enables tool aggregation where requests to a virtual server are routed to appropriate backend servers based on tool schemas and permissions. The vMCP system uses a middleware-based routing layer that matches incoming tool requests to backend servers, handles request/response transformation, and applies policies at the composition boundary.","intents":["I want to present a unified interface to multiple MCP servers without requiring clients to know about each server individually","I need to route tool requests to different backend servers based on the tool being invoked","I want to apply policies and transformations at the composition boundary between client and backend servers"],"best_for":["Platform teams building unified tool interfaces for multiple backend MCP servers","Organizations consolidating tool access through a single gateway endpoint","Teams needing to abstract away backend server complexity from AI agents and clients"],"limitations":["Virtual server routing adds ~40-100ms latency per request due to schema matching and routing decisions","Request/response transformation requires explicit mapping definitions; complex transformations are difficult to maintain","No automatic schema merging; conflicting tool names across backend servers require manual resolution"],"requires":["Backend MCP server definitions with complete schema information","Routing rules mapping tools to backend servers","Transformation specifications for request/response mapping"],"input_types":["Tool invocation requests with tool names and parameters","Backend server configurations","Routing and transformation rule definitions"],"output_types":["Unified MCP server interface","Routed requests to backend servers","Transformed responses back to clients"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_5","uri":"capability://automation.workflow.kubernetes.operator.for.declarative.mcp.server.management","name":"kubernetes operator for declarative mcp server management","description":"ToolHive provides a Kubernetes operator that enables declarative management of MCP servers through Custom Resource Definitions (CRDs). The operator watches for MCP server resource definitions in Kubernetes and automatically creates, updates, and scales corresponding workloads. It integrates with Kubernetes' native resource management, enabling MCP servers to be managed using standard kubectl commands and GitOps workflows. The operator handles workload lifecycle events, health monitoring, and integration with Kubernetes networking and storage systems.","intents":["I want to manage MCP servers using Kubernetes-native tools and workflows (kubectl, GitOps)","I need MCP servers to scale automatically based on Kubernetes resource metrics","I want to integrate MCP server management with my existing Kubernetes infrastructure and monitoring"],"best_for":["Teams already running Kubernetes clusters and wanting to manage MCP servers natively","Organizations using GitOps workflows (ArgoCD, Flux) for infrastructure management","Enterprises needing tight integration between MCP server lifecycle and Kubernetes resource management"],"limitations":["Requires Kubernetes 1.19+ with CRD support; not suitable for non-Kubernetes environments","Operator installation and RBAC configuration adds operational complexity","CRD validation and webhook processing add ~100-200ms to resource creation operations"],"requires":["Kubernetes cluster 1.19 or later","kubectl access with cluster-admin or operator installation permissions","ToolHive operator Helm chart or YAML manifests for installation"],"input_types":["Kubernetes Custom Resource Definitions (CRDs) for MCP servers","ConfigMaps for server configurations","Secrets for credential management"],"output_types":["Kubernetes Deployments/StatefulSets for MCP servers","Services for network exposure","Events for lifecycle tracking"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_6","uri":"capability://tool.use.integration.transport.protocol.abstraction.with.multiple.scheme.support","name":"transport protocol abstraction with multiple scheme support","description":"ToolHive abstracts MCP server communication through a transport protocol layer that supports multiple transport schemes (stdio, HTTP, WebSocket, gRPC) without requiring changes to server logic. The system uses a protocol scheme abstraction that maps incoming requests to appropriate transport handlers, enabling clients to communicate with MCP servers via different protocols depending on deployment context. This allows the same MCP server implementation to be accessed via stdio locally, HTTP in cloud environments, or gRPC for high-performance scenarios.","intents":["I want to access the same MCP server via different protocols (stdio, HTTP, WebSocket) depending on my deployment context","I need to support legacy clients that only understand specific transport protocols","I want to optimize transport protocol selection based on performance requirements and network topology"],"best_for":["Teams supporting diverse client types with different protocol requirements","Organizations migrating MCP servers across different deployment environments with varying protocol support","Performance-sensitive applications needing to choose optimal transport protocols"],"limitations":["Protocol abstraction adds ~10-30ms overhead per request due to marshaling/unmarshaling across protocol boundaries","Not all MCP server features are equally supported across all protocols; some features may be protocol-specific","WebSocket and gRPC support require additional dependencies and configuration"],"requires":["MCP server implementation compatible with ToolHive's protocol abstraction","Protocol-specific client libraries for non-stdio transports","Network configuration supporting chosen transport protocols"],"input_types":["MCP requests in protocol-specific format (JSON-RPC for HTTP, protobuf for gRPC, etc.)","Protocol scheme specifications"],"output_types":["MCP responses in protocol-specific format","Protocol-agnostic internal representation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_7","uri":"capability://automation.workflow.cli.based.workload.management.with.configuration.builders","name":"cli-based workload management with configuration builders","description":"ToolHive provides a comprehensive CLI interface (thv command) for managing MCP server workloads with configuration builders that generate RunConfig specifications from command-line flags and interactive prompts. The CLI supports commands like `thv run` for starting servers, `thv proxy` for gateway operations, `thv registry` for server discovery, and `thv client` for client registration. The configuration builder system translates CLI inputs into structured RunConfig YAML/JSON, enabling users to define complex workload specifications without manually writing configuration files.","intents":["I want to start MCP servers from the command line without writing configuration files","I need to register clients and manage authentication through CLI commands","I want to discover available MCP servers and their capabilities through CLI queries"],"best_for":["Developers running MCP servers locally during development","DevOps teams automating MCP server deployment through scripts","Teams preferring CLI-based workflows over configuration file management"],"limitations":["CLI flags can become unwieldy for complex configurations with many options; configuration files are more maintainable","Interactive prompts require human input; not suitable for fully automated deployments","CLI output parsing is fragile; programmatic access should use REST API instead"],"requires":["ToolHive binary installed and in PATH","Bash or compatible shell for CLI usage","Appropriate permissions for creating processes and accessing resources"],"input_types":["CLI flags and arguments","Interactive prompt responses","Configuration file paths"],"output_types":["Running MCP server processes","Generated RunConfig specifications","CLI command output and status messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_8","uri":"capability://automation.workflow.rest.api.for.programmatic.workload.and.registry.management","name":"rest api for programmatic workload and registry management","description":"ToolHive exposes a comprehensive REST API for programmatic management of MCP server workloads, registry operations, client management, and configuration. The API follows standard REST conventions with JSON request/response bodies and includes endpoints for workload lifecycle operations (create, read, update, delete, list), registry queries, client registration, and health checks. The API is documented via OpenAPI/Swagger specifications and supports authentication through bearer tokens or custom auth schemes, enabling integration with external orchestration systems and CI/CD pipelines.","intents":["I want to automate MCP server deployment and management through CI/CD pipelines","I need to integrate ToolHive with external orchestration systems and monitoring tools","I want to programmatically query the MCP server registry and discover available tools"],"best_for":["DevOps teams automating MCP server lifecycle through infrastructure-as-code","Organizations integrating ToolHive with existing CI/CD and orchestration platforms","Teams building custom management interfaces or dashboards on top of ToolHive"],"limitations":["API rate limiting may be required in high-throughput scenarios; no built-in rate limiting documented","Eventual consistency for distributed deployments; immediate consistency not guaranteed across all nodes","API versioning strategy not clearly documented; breaking changes may occur between versions"],"requires":["ToolHive server running and accessible via HTTP","Authentication credentials (bearer token or custom auth scheme)","HTTP client library for making REST requests"],"input_types":["JSON request bodies with workload specifications","Query parameters for filtering and pagination","Authentication headers"],"output_types":["JSON response bodies with workload status and metadata","HTTP status codes indicating success/failure","OpenAPI/Swagger documentation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stacklok--toolhive__cap_9","uri":"capability://safety.moderation.middleware.architecture.for.request.interception.and.policy.enforcement","name":"middleware architecture for request interception and policy enforcement","description":"ToolHive implements a middleware architecture that intercepts requests before MCP server execution, enabling cross-cutting concerns like authentication, authorization, credential injection, logging, and policy enforcement. The middleware chain is composable, allowing operators to define the order and combination of middleware components. Each middleware can inspect and modify requests, apply policies, inject credentials, and log events without requiring changes to MCP server implementations. This architecture enables flexible policy enforcement and observability without coupling policies to server logic.","intents":["I want to enforce organizational policies on all MCP tool invocations without modifying server code","I need to inject credentials and apply transformations to requests before they reach MCP servers","I want to log and audit all tool invocations for compliance and debugging purposes"],"best_for":["Platform teams implementing cross-cutting security and compliance policies","Organizations needing to enforce consistent behavior across diverse MCP servers","Teams requiring detailed observability and audit trails of tool usage"],"limitations":["Middleware chain processing adds ~50-150ms latency depending on number and complexity of middleware components","Middleware ordering is critical; incorrect ordering can cause policy bypass or unexpected behavior","Debugging middleware interactions can be complex; requires detailed logging and tracing"],"requires":["Middleware component implementations (built-in or custom)","Middleware configuration specifying order and parameters","Logging and tracing infrastructure for observability"],"input_types":["MCP tool invocation requests","Middleware configuration specifications","Policy definitions"],"output_types":["Modified requests with injected credentials and policy enforcement","Audit logs and traces","Policy enforcement decisions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Go 1.21+ (for building from source)","Docker or Kubernetes cluster for containerized deployments","Redis instance for distributed state management in multi-node setups","MCP server metadata with schema definitions (OpenAPI/JSON Schema format)","Vector embedding service or local embedding model for semantic search","Permission profile definitions in YAML/JSON format","Container registry with image scanning capabilities","Image signing infrastructure (Sigstore, Cosign, or equivalent)","Attestation verification configuration","Log aggregation system (ELK, Splunk, CloudWatch, etc.)"],"failure_modes":["Container runtime abstraction adds ~50-100ms overhead per workload startup due to initialization layers","Kubernetes operator requires CRD installation and RBAC configuration; not suitable for serverless/FaaS environments","Local process execution mode lacks process isolation guarantees of containerized runtimes","Semantic search accuracy depends on quality of server metadata and schema descriptions; poor documentation reduces discoverability","Registry lookups add ~20-50ms latency per tool selection operation","No built-in versioning strategy for MCP servers; managing multiple versions requires external coordination","Image scanning adds ~30-60 seconds to deployment time depending on image size and registry","Attestation verification requires integration with signing infrastructure (Sigstore, etc.); adds operational complexity","Scanning results are point-in-time; vulnerabilities discovered after deployment are not automatically detected","Structured logging adds ~5-10ms overhead per request due to serialization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.48767393180377266,"quality":0.5,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:56:59.049Z","last_commit":"2026-05-02T19:31:02Z"},"community":{"stars":1764,"forks":212,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stacklok--toolhive","compare_url":"https://unfragile.ai/compare?artifact=stacklok--toolhive"}},"signature":"FJcUlTacsa7GRI1TjsBH1vFrSJbTv2Q+VmUAdI1fnzYzQBTP9mOT4byoBWoMW4+ydAPaLxo5x99/dU00ejjpBw==","signedAt":"2026-06-20T13:35:11.279Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stacklok--toolhive","artifact":"https://unfragile.ai/stacklok--toolhive","verify":"https://unfragile.ai/api/v1/verify?slug=stacklok--toolhive","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"}}