{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ibm--mcp-context-forge","slug":"ibm--mcp-context-forge","name":"mcp-context-forge","type":"mcp","url":"https://ibm.github.io/mcp-context-forge/","page_url":"https://unfragile.ai/ibm--mcp-context-forge","categories":["mcp-servers"],"tags":["agents","ai","api-gateway","asyncio","authentication-middleware","devops","docker","fastapi","federation","gateway","generative-ai","jwt","kubernetes","llm-agents","mcp","model-context-protocol","observability","prompt-engineering","python","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ibm--mcp-context-forge__cap_0","uri":"capability://tool.use.integration.multi.protocol.mcp.server.federation.with.unified.endpoint.exposure","name":"multi-protocol mcp server federation with unified endpoint exposure","description":"Federates multiple Model Context Protocol (MCP) servers into a single unified HTTP/SSE endpoint using a transport abstraction layer that handles protocol translation. The gateway maintains a ServerRegistry that tracks all connected MCP servers, routes incoming requests through a ToolService that normalizes tool schemas across heterogeneous servers, and exposes both streamable HTTP and SSE transports via FastAPI endpoints (streamable_http_auth, sse_endpoint). This enables clients to interact with dozens of MCP servers through a single gateway URL without managing individual server connections.","intents":["I want to expose multiple MCP servers to Claude Desktop or other clients through a single endpoint without managing individual connections","I need to add new MCP servers to my agent infrastructure without reconfiguring all client applications","I want to abstract away the complexity of managing MCP protocol versions and transport details from my agents"],"best_for":["teams deploying multiple MCP servers in production environments","enterprises managing heterogeneous tool ecosystems across departments","AI platform builders offering MCP as a service to downstream users"],"limitations":["Transport abstraction adds ~50-100ms latency per request due to protocol translation and routing overhead","Server discovery is static (requires gateway restart to add new MCP servers unless using dynamic configuration)","No built-in load balancing across multiple instances of the same MCP server"],"requires":["Python 3.9+","FastAPI 0.100+","MCP servers compatible with stdio or SSE transport","Docker or Kubernetes for production deployment"],"input_types":["MCP protocol messages (JSON-RPC 2.0 format)","Tool invocation requests with parameters","Resource read/list requests"],"output_types":["Normalized tool schemas (JSON)","Tool execution results","Streaming responses via SSE"],"categories":["tool-use-integration","api-gateway"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_1","uri":"capability://safety.moderation.centralized.authentication.and.authorization.with.rbac.and.multi.tenancy","name":"centralized authentication and authorization with rbac and multi-tenancy","description":"Implements a middleware-based authentication system (RBAC middleware in mcpgateway/middleware/rbac.py) that enforces role-based access control across all federated servers and tools. The gateway supports JWT token validation, OAuth/SSO integration, and multi-tenant isolation via a SessionRegistry that tracks authenticated sessions and their associated permissions. Each request is validated against a permission matrix that maps users/teams to allowed tools and servers, with enforcement happening at the gateway layer before requests reach downstream MCP servers or APIs.","intents":["I need to enforce fine-grained access control so different teams can only invoke specific tools they're authorized for","I want to implement multi-tenant isolation where one organization's tools are invisible to another organization","I need to audit which users invoked which tools and when, with centralized logging"],"best_for":["enterprises with multiple teams/departments sharing a single gateway","SaaS platforms offering MCP tools to multiple customers","organizations with strict compliance requirements (SOC2, HIPAA) requiring audit trails"],"limitations":["RBAC evaluation adds ~20-50ms per request for permission matrix lookups","No dynamic permission updates without redeploying or restarting the gateway (permissions are loaded at startup)","JWT token revocation requires external token blacklist management (not built-in)"],"requires":["JWT-compatible identity provider or OAuth2 server","Configuration of RBAC rules in config.yaml or environment variables","SQLAlchemy-compatible database for session persistence"],"input_types":["JWT tokens in Authorization header","OAuth2 authorization codes","User identity claims"],"output_types":["Validated session tokens","Permission decision (allow/deny)","Audit log entries"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_10","uri":"capability://safety.moderation.tool.execution.guardrails.and.policy.enforcement.with.pre.post.execution.hooks","name":"tool execution guardrails and policy enforcement with pre/post-execution hooks","description":"Implements a guardrail system that enforces policies on tool execution through pre-execution validation and post-execution result filtering. Pre-execution hooks validate tool invocations against policies (e.g., rate limits, cost budgets, parameter constraints) and can reject or modify requests. Post-execution hooks filter or transform results based on policies (e.g., redact sensitive data, enforce output size limits). Policies are defined declaratively in configuration and can be customized per tool, user, or team. The guardrail system integrates with the plugin system, allowing custom policies to be implemented as plugins.","intents":["I want to prevent tools from being invoked too frequently (rate limiting) or exceeding cost budgets","I need to enforce parameter validation (e.g., file paths must be within allowed directories) before tools execute","I want to redact sensitive data from tool results before returning them to clients"],"best_for":["organizations with strict governance requirements (cost control, security policies)","platforms offering tools to untrusted users and needing to prevent abuse","teams managing sensitive data and needing to enforce data residency policies"],"limitations":["Guardrail evaluation adds ~20-50ms per request","Complex policies can be difficult to reason about and debug","No built-in policy versioning or A/B testing for policy changes"],"requires":["Policy definitions in configuration (YAML or environment variables)","Custom plugins for complex policy logic"],"input_types":["Tool invocation requests","Tool execution results"],"output_types":["Policy decision (allow/deny/modify)","Modified requests/responses"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_11","uri":"capability://automation.workflow.export.and.import.of.tool.definitions.and.gateway.configuration.for.backup.and.migration","name":"export and import of tool definitions and gateway configuration for backup and migration","description":"Provides export/import functionality that enables administrators to backup and migrate gateway state (tool definitions, RBAC rules, plugin configurations) between gateway instances. Export generates a JSON or YAML file containing all gateway configuration and tool metadata. Import reads this file and restores the gateway state, enabling disaster recovery and environment promotion (dev → staging → prod). The export/import system preserves all metadata and relationships, enabling lossless round-trip migrations.","intents":["I want to backup my gateway configuration and tool definitions for disaster recovery","I need to promote tool definitions from dev to staging to production without manual reconfiguration","I want to migrate from one gateway instance to another with zero downtime"],"best_for":["teams managing multiple gateway instances across environments","organizations with strict disaster recovery requirements","platforms offering gateway-as-a-service with customer data migration"],"limitations":["Export/import does not preserve runtime state (active sessions, cache contents)","Large exports (thousands of tools) can be slow and memory-intensive","No built-in validation that imported configuration is compatible with target gateway version"],"requires":["Admin API access to source and target gateways","Sufficient disk space for export files"],"input_types":["Export request (format: JSON or YAML)","Import file (JSON or YAML)"],"output_types":["Export file (JSON or YAML)","Import status (success/failure)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_12","uri":"capability://automation.workflow.kubernetes.native.deployment.with.helm.charts.and.auto.scaling","name":"kubernetes-native deployment with helm charts and auto-scaling","description":"Provides production-ready Kubernetes deployment through Helm charts (in charts/mcp-stack/) that configure the gateway, database, Redis cache, and nginx ingress as a complete stack. The Helm charts support auto-scaling based on metrics (CPU, memory, request latency), enabling the gateway to scale horizontally under load. Deployment includes health checks (liveness and readiness probes), resource limits, and pod disruption budgets for high availability. The charts are parameterized to support multiple environments (dev, staging, prod) through Helm values overrides.","intents":["I want to deploy the gateway to Kubernetes with production-grade reliability and auto-scaling","I need to manage multiple gateway instances across environments with consistent configuration","I want to ensure the gateway is highly available and can survive node failures"],"best_for":["organizations running Kubernetes clusters","teams requiring auto-scaling and high availability","platforms offering managed Kubernetes services (EKS, GKE, AKS)"],"limitations":["Requires Kubernetes 1.20+ and Helm 3.0+","Auto-scaling requires metrics-server and custom metrics (Prometheus) to be installed","Helm charts may require customization for non-standard Kubernetes environments"],"requires":["Kubernetes 1.20+","Helm 3.0+","Persistent volume provisioner for database and cache","Ingress controller (nginx, Traefik, etc.)"],"input_types":["Helm values (YAML)"],"output_types":["Kubernetes resources (Deployments, Services, ConfigMaps, Secrets)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_13","uri":"capability://automation.workflow.docker.compose.deployment.for.local.development.and.testing","name":"docker compose deployment for local development and testing","description":"Provides a Docker Compose configuration (docker-compose.yml) that spins up a complete local development environment with the gateway, PostgreSQL database, Redis cache, and nginx reverse proxy. The Compose file includes environment variable configuration, volume mounts for code changes (enabling hot-reload during development), and networking setup. This enables developers to run the entire gateway stack locally without installing dependencies, facilitating rapid iteration and testing.","intents":["I want to set up a local development environment for the gateway without installing Python, PostgreSQL, Redis, etc.","I need to test the gateway with realistic infrastructure (database, cache, reverse proxy) locally","I want to enable hot-reload during development so code changes are reflected immediately"],"best_for":["developers contributing to the gateway codebase","teams prototyping custom gateway configurations locally","CI/CD pipelines that need to run integration tests"],"limitations":["Docker Compose is not suitable for production (use Kubernetes instead)","Performance is degraded compared to native installation due to container overhead","Volume mounts for hot-reload may not work reliably on Windows/Mac with Docker Desktop"],"requires":["Docker 20.10+","Docker Compose 2.0+","4GB+ RAM available for containers"],"input_types":["docker-compose.yml configuration"],"output_types":["Running containers (gateway, database, cache, reverse proxy)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_2","uri":"capability://memory.knowledge.intelligent.response.caching.with.redis.backend.and.cache.invalidation","name":"intelligent response caching with redis backend and cache invalidation","description":"Implements a multi-layer caching strategy using Redis as the distributed cache backend, with cache keys derived from tool name, parameters, and user context. The gateway caches tool invocation results based on configurable TTL policies and cache invalidation rules (e.g., invalidate cache for tool X when tool Y is invoked). Cache hits bypass downstream MCP servers entirely, reducing latency and load. The caching layer is transparent to clients and respects RBAC boundaries (cached results are isolated per user/team).","intents":["I want to reduce latency for frequently-called tools by caching their results at the gateway","I need to ensure cached results don't leak between tenants or users with different permissions","I want to invalidate caches when upstream data changes (e.g., clear file listing cache when a file is written)"],"best_for":["deployments with high tool invocation volume and read-heavy workloads","tools with expensive computations or external API calls that benefit from caching","multi-tenant environments where cache isolation is critical"],"limitations":["Cache invalidation is manual (requires explicit configuration of invalidation rules) — no automatic dependency tracking","Redis becomes a single point of failure for cache layer (requires Redis HA setup for production)","Cache key collisions possible if parameter hashing is not carefully designed"],"requires":["Redis 6.0+ instance (local or remote)","REDIS_URL environment variable or config.redis_url setting","Tool definitions must declare cacheable=true to enable caching"],"input_types":["Tool invocation requests with parameters","Cache invalidation events"],"output_types":["Cached tool results (JSON)","Cache hit/miss metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_3","uri":"capability://tool.use.integration.protocol.translation.and.multi.transport.endpoint.exposure.http.sse.grpc","name":"protocol translation and multi-transport endpoint exposure (http, sse, grpc)","description":"Exposes the same underlying tool registry through multiple transport protocols simultaneously: streamable HTTP with authentication (streamable_http_auth endpoint), Server-Sent Events (SSE) for streaming responses, and gRPC for high-performance integrations. The transport layer abstracts protocol-specific details (request/response serialization, streaming semantics, error handling) through a common interface, allowing clients to choose their preferred transport without gateway reconfiguration. This is implemented via transport adapters that translate between MCP JSON-RPC messages and protocol-specific formats.","intents":["I want to support both HTTP and gRPC clients without running separate gateway instances","I need streaming responses from long-running tools without polling","I want to integrate with legacy systems that only speak gRPC or REST"],"best_for":["polyglot environments with clients using different transport preferences","high-performance integrations where gRPC latency matters","browser-based clients that require SSE or WebSocket streaming"],"limitations":["gRPC transport requires protobuf schema generation and client library compilation","SSE streaming has higher memory overhead than HTTP polling for high-concurrency scenarios","Protocol translation adds ~30-80ms latency depending on message size and transport complexity"],"requires":["FastAPI 0.100+ for HTTP/SSE endpoints","gRPC Python libraries (grpcio, grpcio-tools) for gRPC transport","Protocol buffer definitions for gRPC service schema"],"input_types":["HTTP POST requests (JSON)","gRPC method calls (protobuf)","SSE client subscriptions"],"output_types":["HTTP responses (JSON)","gRPC responses (protobuf)","SSE event streams"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_4","uri":"capability://tool.use.integration.dynamic.tool.discovery.and.schema.normalization.across.heterogeneous.servers","name":"dynamic tool discovery and schema normalization across heterogeneous servers","description":"Implements a ToolService that discovers all available tools from federated MCP servers, normalizes their schemas into a unified format, and exposes them via a discovery API. The gateway periodically polls connected servers for tool updates, caches the normalized schemas, and serves them to clients through a single /tools endpoint. Schema normalization handles differences in parameter types, descriptions, and required fields across servers, presenting a consistent interface to clients regardless of upstream server implementation details.","intents":["I want to discover all available tools across my MCP server fleet without querying each server individually","I need a consistent tool schema format even though my servers use different parameter conventions","I want to expose tool metadata (descriptions, categories, required permissions) to my agents for intelligent tool selection"],"best_for":["agents that need to dynamically select tools based on task requirements","platforms offering tool discovery UIs to end users","teams managing large tool inventories across multiple servers"],"limitations":["Schema discovery is periodic (not real-time) — new tools may not appear for up to the polling interval (default 60s)","Schema normalization may lose server-specific metadata or custom fields","No built-in schema versioning — breaking changes in tool schemas are not tracked"],"requires":["MCP servers must implement the tools/list and tools/describe endpoints","Polling interval configurable via TOOL_DISCOVERY_INTERVAL environment variable"],"input_types":["MCP tools/list responses","MCP tools/describe responses"],"output_types":["Normalized tool schema (JSON)","Tool metadata (name, description, parameters, required fields)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_5","uri":"capability://tool.use.integration.plugin.system.with.extensible.middleware.and.custom.tool.handlers","name":"plugin system with extensible middleware and custom tool handlers","description":"Provides a plugin architecture that allows developers to extend gateway behavior through custom middleware, tool handlers, and event listeners. Plugins are loaded from a plugins directory, registered with the FastAPI application, and can hook into request/response lifecycle events (pre-tool-invocation, post-tool-invocation, on-error). The plugin system uses a standardized interface (BasePlugin class) that plugins implement to add custom logic like request transformation, response filtering, or integration with external systems. Plugins have access to the gateway's service layer (ToolService, GatewayService) for deep integration.","intents":["I want to add custom request validation or transformation logic without modifying the gateway core","I need to integrate with external systems (logging, monitoring, approval workflows) when tools are invoked","I want to implement custom tool execution policies (rate limiting, cost tracking) specific to my organization"],"best_for":["organizations with custom governance or compliance requirements","teams building specialized gateways for specific domains (healthcare, finance)","platforms offering extensibility to downstream users"],"limitations":["Plugin API is not stable — breaking changes may occur between gateway versions","Plugins run in the same process as the gateway — a misbehaving plugin can crash the entire gateway","No plugin sandboxing or resource limits — plugins have full access to gateway state"],"requires":["Python 3.9+","Understanding of FastAPI middleware patterns","Plugin must inherit from BasePlugin and implement required methods"],"input_types":["Tool invocation requests","Gateway lifecycle events"],"output_types":["Modified requests/responses","Side effects (logging, external API calls)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_6","uri":"capability://automation.workflow.observability.and.monitoring.with.structured.logging.and.metrics.export","name":"observability and monitoring with structured logging and metrics export","description":"Provides comprehensive observability through structured logging (JSON format with context fields), metrics collection (request latency, cache hit rates, tool invocation counts), and integration with monitoring backends (Prometheus, OpenTelemetry). The gateway logs all tool invocations with context (user, team, tool name, parameters, result, duration), enabling audit trails and performance analysis. Metrics are exported in Prometheus format and can be scraped by monitoring systems. Distributed tracing support (via OpenTelemetry) enables end-to-end request tracking across the gateway and downstream services.","intents":["I need to audit which users invoked which tools and when for compliance and debugging","I want to monitor gateway performance (latency, throughput, error rates) and alert on anomalies","I need to trace requests end-to-end from client through gateway to downstream MCP servers"],"best_for":["production deployments requiring audit trails and compliance reporting","teams operating large-scale tool infrastructure with performance SLOs","organizations with centralized monitoring and observability platforms"],"limitations":["Structured logging adds ~10-20ms per request due to JSON serialization and I/O","Metrics cardinality can explode if tool names or user IDs are high-cardinality (requires careful metric design)","OpenTelemetry integration requires external collector (e.g., Jaeger, Datadog) for storage and visualization"],"requires":["Logging backend (stdout, file, or external service like ELK)","Prometheus-compatible metrics scraper for metrics collection","OpenTelemetry collector (optional, for distributed tracing)"],"input_types":["Tool invocation requests","Gateway lifecycle events"],"output_types":["Structured log entries (JSON)","Prometheus metrics (text format)","OpenTelemetry spans"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_7","uri":"capability://automation.workflow.configuration.management.with.environment.variables.yaml.and.runtime.updates","name":"configuration management with environment variables, yaml, and runtime updates","description":"Supports multiple configuration sources (environment variables, YAML files, environment-specific overrides) with a unified config schema (defined in config.py and config.schema.json). Configuration is loaded at startup and can be partially updated at runtime through the admin API without full gateway restart. The config system uses Pydantic for validation and type-checking, ensuring invalid configurations are caught early. Sensitive values (API keys, database credentials) are stored in .env files or secrets management systems and are not logged or exposed in debug output.","intents":["I want to configure the gateway for different environments (dev, staging, prod) without code changes","I need to update tool definitions or RBAC rules without restarting the gateway","I want to manage secrets securely without embedding them in configuration files"],"best_for":["teams deploying to multiple environments with different configurations","organizations using infrastructure-as-code (Terraform, Helm) for gateway deployment","teams requiring secrets management integration (Vault, AWS Secrets Manager)"],"limitations":["Some configuration changes require gateway restart (e.g., changing database connection string)","No configuration versioning or rollback — changes are applied immediately","YAML configuration files are not validated against schema until runtime"],"requires":["Python 3.9+","Pydantic 2.0+ for configuration validation","YAML parser (PyYAML)"],"input_types":["Environment variables","YAML configuration files","Admin API requests"],"output_types":["Validated configuration object","Configuration change events"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_8","uri":"capability://tool.use.integration.agent.to.agent.a2a.gateway.for.agent.to.agent.communication.and.coordination","name":"agent-to-agent (a2a) gateway for agent-to-agent communication and coordination","description":"Provides an A2A gateway that enables agents to discover and invoke other agents through a unified endpoint, similar to how agents invoke tools. The A2A gateway maintains an agent registry, handles agent authentication and authorization, and routes agent-to-agent requests through the same middleware stack (RBAC, caching, observability) as tool invocations. This enables complex multi-agent workflows where agents can coordinate with each other without direct peer-to-peer connections, with all communication flowing through the gateway for governance and observability.","intents":["I want to enable agents to discover and invoke other agents without hardcoding agent endpoints","I need to enforce access control between agents (e.g., agent A can invoke agent B but not agent C)","I want to monitor and audit agent-to-agent communication for debugging and compliance"],"best_for":["multi-agent systems with complex coordination requirements","organizations running multiple specialized agents that need to collaborate","platforms offering agent-as-a-service to multiple teams"],"limitations":["A2A communication adds latency compared to direct agent-to-agent connections","Agent discovery is static (requires gateway restart to add new agents)","No built-in deadlock detection for circular agent dependencies"],"requires":["Agents must be registered in the gateway's agent registry","Agents must support the A2A protocol (JSON-RPC over HTTP/gRPC)"],"input_types":["Agent invocation requests","Agent discovery queries"],"output_types":["Agent invocation results","Agent metadata and capabilities"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ibm--mcp-context-forge__cap_9","uri":"capability://automation.workflow.session.management.and.event.streaming.for.real.time.gateway.state.updates","name":"session management and event streaming for real-time gateway state updates","description":"Implements a SessionRegistry that tracks active sessions (authenticated user connections) with associated metadata (user ID, team, permissions, session start time). The gateway emits events for significant state changes (tool invocation, cache invalidation, permission changes) through an event service that can be consumed by clients via WebSocket or SSE. This enables real-time updates to clients when gateway state changes, supporting use cases like live tool execution monitoring and collaborative tool usage tracking.","intents":["I want to track active sessions and see which users are currently using the gateway","I need to push real-time updates to clients when tools are invoked or cache is invalidated","I want to implement collaborative features where multiple users can see each other's tool invocations"],"best_for":["real-time monitoring dashboards that need live updates","collaborative environments where multiple users interact with shared tools","platforms offering session management and user activity tracking"],"limitations":["Event streaming adds memory overhead for maintaining open connections","Session state is not persisted across gateway restarts (sessions are lost)","Event ordering is not guaranteed in distributed deployments with multiple gateway instances"],"requires":["WebSocket or SSE support in client","SQLAlchemy-compatible database for session persistence"],"input_types":["Session creation/termination events","Tool invocation events","State change events"],"output_types":["Session metadata","Event streams (JSON)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastAPI 0.100+","MCP servers compatible with stdio or SSE transport","Docker or Kubernetes for production deployment","JWT-compatible identity provider or OAuth2 server","Configuration of RBAC rules in config.yaml or environment variables","SQLAlchemy-compatible database for session persistence","Policy definitions in configuration (YAML or environment variables)","Custom plugins for complex policy logic","Admin API access to source and target gateways"],"failure_modes":["Transport abstraction adds ~50-100ms latency per request due to protocol translation and routing overhead","Server discovery is static (requires gateway restart to add new MCP servers unless using dynamic configuration)","No built-in load balancing across multiple instances of the same MCP server","RBAC evaluation adds ~20-50ms per request for permission matrix lookups","No dynamic permission updates without redeploying or restarting the gateway (permissions are loaded at startup)","JWT token revocation requires external token blacklist management (not built-in)","Guardrail evaluation adds ~20-50ms per request","Complex policies can be difficult to reason about and debug","No built-in policy versioning or A/B testing for policy changes","Export/import does not preserve runtime state (active sessions, cache contents)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5750583009440345,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:56:59.048Z","last_commit":"2026-05-03T12:53:28Z"},"community":{"stars":3650,"forks":640,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ibm--mcp-context-forge","compare_url":"https://unfragile.ai/compare?artifact=ibm--mcp-context-forge"}},"signature":"09BQdOqQYsoGuSW3WD0ODdfwhN6QBG4EGevy0kTrq/N7tq3eMQ4vXVThH8WCqihzPtuYZKM2gqZhJoEaZ0IIDA==","signedAt":"2026-06-22T05:28:04.312Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ibm--mcp-context-forge","artifact":"https://unfragile.ai/ibm--mcp-context-forge","verify":"https://unfragile.ai/api/v1/verify?slug=ibm--mcp-context-forge","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"}}