IBM/mcp-context-forge

Features

• AI Tool Federation - Unifies diverse AI tool servers and APIs into a single consistent interface for agent discovery and execution.
• MCP Gateway Federations - Acts as a centralized gateway that unifies multiple MCP servers and APIs for AI agent discovery and execution.
• Aggregator Servers - Unifies multiple tools, APIs, and MCP servers into a single consistent interface for AI agent discovery.
• AI Protocol Translation - Translates communication between local standard I/O servers and remote streaming HTTP endpoints using AI-specific context protocols.
• Protocol Translation Bridges - Implements a translation bridge that converts communication between standard I/O, SSE, gRPC, and REST for tool server interoperability.
• Agent Communication Interfaces - Normalizes requests and serializes responses to ensure consistent transport behavior and retry logic across agents.
• Agent-to-Agent Communication - Implements standardized interfaces and metrics to facilitate communication between distributed AI agents.
• Agent Framework Integrations - Connects various AI agent frameworks to a centralized registry for standardized access to tools and resources.
• Tool Execution Monitoring - Tracks tool invocations, prompt rendering, and resource fetching, including detailed timing and execution results.
• Agent Message Routing - Provides specialized dispatch logic to direct communication and requests between different AI agents.
• Agent Registrations - Manages the registration of external agents by specifying their endpoint URLs and authentication credentials.
• Custom Tool Registrations - Allows the definition of executable operations for APIs by specifying target URLs and input schemas for agent use.
• AI Agent Orchestrators - Facilitates the integration and discovery of workflows involving multiple external AI agents through a centralized interface.
• MCP Server Registrations - Connects external tool servers to a central gateway to enable unified discovery of available capabilities.
• AI Agent Tool Integrations - Connects AI models to external software and APIs, enabling agents to call specific functions from a chat interface.
• Multi-Protocol Tool Exposures - Exposes agent capabilities as tools compatible with multiple protocols, including MCP, REST, and gRPC.
• AI Governance Tools - Implements an infrastructure layer for managing access control, role-based permissions, and security guardrails for AI tools.
• AI Observability Tracing - Tracks distributed requests and LLM-specific metrics using OTLP backends to provide AI-specific observability.
• Dynamic Tool Discovery - Fetches available tools and their schemas from remote servers in real-time without manual configuration.
• External Agent Integrations - Integrates external agents via a standardized interface to allow discovery and remote invocation.
• Federated Tool Execution - Invokes tools across different registered gateways through a single consistent interface for AI agents.
• Agent-as-Tool Wrapping - Allows AI agents to be wrapped and exposed as standardized tools for use by other agents.
• Prompt Management Systems - Defines and manages parameterized message templates to ensure consistency in system instructions for AI agents.
• LLM Evaluation Frameworks - Implements a testing environment for assessing model output quality and grounding using automated evaluation suites.
• LLM Provider Integrations - Manages connectivity and configuration for various AI model providers to control tool-calling behavior.
• MCP Lifecycle Orchestration - Manages the full operational lifecycle of the MCP stack, from configuration validation to container deployment.
• MCP Protocol Adapters - Translates REST APIs and gRPC services into compatible tools through automatic schema extraction.
• Action Function Exposures - Exposes functions with JSON Schema definitions and authentication for seamless invocation by language models.
• Resource Exposure - Provides a unified interface for exposing tools, prompts, and read-only data resources to AI agents.
• Server Registries - Registers multiple servers and APIs into a single proxy to enable centralized discovery.
• Tool Invocation Handlers - Executes tools from federated servers via HTTP endpoints using JSON payloads.
• Tool Registries - Provides a centralized registry to track, discover, and verify available AI tools.
• Tool-Use Proxies - Provides a proxy layer that handles the routing and execution of tool requests to remote servers.
• Virtual API Wrappers - Wraps REST and gRPC services as MCP-compliant tools using automatic reflection and schema extraction.
• Tool Access Governance - The project enforces RBAC and visibility filtering to restrict tool exposure to specific users.
• AI Context Resource Management - Lists available data resources and reads specific content using unique URIs to provide context for agents.
• Remote Function Execution - Executes custom functions on federated servers through a centralized gateway.
• Tag-Based Search - Provides capabilities for retrieving specific tools or resources based on assigned metadata labels and tags.
• Python Execution Sandboxes - Executes Python scripts in a protected environment using AST-level transformation and optional sandboxing.
• Virtual Tool Conversions - Transforms REST-backed services into virtual servers and tools discoverable by AI agents.
• gRPC Service Discovery - Automatically discovers gRPC services and converts them into compatible tools for AI agents.
• Tool Execution Interfaces - Implements JSON-RPC endpoints that accept tool call requests with specific arguments and return execution results.
• MCP Transport Protocol Supports - Supports sending requests via diverse transport protocols including JSON-RPC, streaming events, and standard I/O.
• Stdio to SSE Translation - Translates standard I/O servers into streaming services to enable remote network access.
• Protocol Client Integrations - Supports multiple communication protocols, including stdio and SSE, for connecting AI clients to federated tools.
• Protocol Translation - Translates standard I/O servers into streaming endpoints to enable network-based registration with the proxy.
• gRPC-to-MCP Converters - Translates gRPC services into MCP-compatible tools by converting Protobuf messages to JSON.
• AI Tool Routing - Directs requests using MCP-specific logic and tool registries to ensure precise tool invocation.
• Stdio Tunnels - Wraps standard input/output servers in a bridge to expose capabilities over the network via SSE.
• Transport Bridges - Translates traffic between disparate transport layers like stdio, SSE, gRPC, and REST to enable tool server interoperability.
• Sandboxed Code Execution Environments - Runs dynamic Python code in isolated contexts with AST transformations and resource limits.
• Rust Runtime Accelerations - Increases throughput by moving the high-traffic data path to a high-performance Rust-based runtime.
• Agentic - Governs AI tool access using user-scoped OAuth tokens, JWTs, and RBAC context.
• Automatic Request Token Injection - Automatically retrieves stored user tokens and injects them as Bearer headers into forwarded upstream requests.
• Centralized Governance Platforms - Enforces authentication, token scoping, and RBAC from a central authority across all protocol modules.
• Enterprise Identity Providers - Integrates with external identity providers via OIDC and SAML to centralize enterprise authentication.
• Identity and Access Management - Resolves caller identity and validates permissions to ensure users only access authorized tools and resources.
• Bearer Token Authentication - Provides a unified authentication layer supporting JWT bearer tokens, Basic Auth, and email logins.
• OAuth Authentication - Handles standard OAuth 2.0 Client Credentials and Authorization Code flows using bearer tokens and PKCE.
• Role-Based Access Control - Enforces granular permissions and visibility levels across tools, resources, and prompts via role-based access.
• Conditional Access Rules - Evaluates dynamic attributes and conditions to restrict plugin execution based on servers, tenants, and users.
• User Identity Management - Manages user registration, authentication, and password complexity enforcement for the platform.
• Natural Language Tool Interfaces - Provides a conversational interface where language models automatically select and execute tools using natural language descriptions.
• MCP Tool Discovery Metadata - Exposes tool metadata including names and input schemas to enable discovery and invocation of federated tools.
• AI Agent Observability - Instruments tool invocations and prompt rendering with distributed tracing and metrics via OpenTelemetry.
• LLM Performance Monitoring - Tracks token usage, API costs, and response quality to identify model drift and monitor LLM performance.
• OpenTelemetry-Integrated Monitors - Integrates with OpenTelemetry and Prometheus to export distributed traces and system metrics.
• LLM Evaluation - Provides a framework for testing model output quality, safety, and grounding using automated suites and LLM-as-a-judge.
• Agentic API Translation - Converts conversational requests into structured API calls and formats resulting data for AI consumption.
• Assistant Lifecycle Management - Manages the registration and state of agent-to-agent assistants and their associated toolsets.
• Agent Evaluation Tools - Assesses the reasoning chains, tool selection patterns, and output quality of autonomous AI agents.
• CLI Process Wrapping - Wraps arbitrary command-line interfaces and subprocesses as AI agents by managing standard I/O streams.
• Agent Prompt Templates - Defines and manages reusable system prompt structures to standardize interactions and control agent behavior.
• OpenAI-Compatible APIs - Exposes standard chat completion endpoints to act as a unified provider for internal services using the OpenAI API format.
• Connection Pooling - Maintains persistent connections to upstream servers to reduce latency for repeated tool calls.
• AI Knowledge Management - Creates and stores named content in Markdown or JSON to provide context for AI agents.
• Prompt Evaluation Tools - Detects ambiguity in prompts and tests output consistency across multiple runs to optimize prompt engineering.
• Prompt Template Retrieval - Lists available templates and fetches specific prompts with arguments for AI agents.
• AI Safety Guardrails - Detects and redacts PII and filters content using moderation models to ensure AI safety.
• Document Grounding - Verifies that AI-generated responses are strictly grounded in retrieved source documents and adhere to instructions.
• Execution Interception Hooks - Modifies prompts and tool calls via execution hooks to implement safety and validation guardrails.
• Server Connection Managers - Establishes real-time connections to virtual servers using Server-Sent Events for data streaming.
• External Service Integrations - Connects to remote plugin services to offload complex inference tasks or enforce security policies.
• RAG Pipeline Integrations - Wraps external tool calls as query engines or retrievers to provide agents with external grounding data.
• Gateway Management - Provides administrative control over gateways that connect the system to external tool servers.
• LLM Gateways - Acts as a centralized gateway exposing an OpenAI-compatible interface for internal LLM endpoints.
• Model Comparison Interfaces - Provides analytical and side-by-side comparison of outputs generated by different model versions to identify superior performance.
• Adversarial Robustness Testing - Evaluates model stability and security by executing adversarial attacks and stability checks across input variations.
• Conversational Interfaces - Provides a conversational interface for AI agents to select and execute available tools using various language model providers.
• MCP Server Synchronizations - Synchronizes tools and resources from remote compatible servers into a centralized local catalog.
• Middleware Extensions - Interposes middleware components into MCP and agentic flows for security and observability.
• Transport Configurations - Configures high-performance communication protocols for connectivity between the gateway and its plugins.
• Invocation Rate Limits - Caps the number of requests clients can make to a specific tool within a time window.
• Evaluation Tool Exposure - Serves LLM evaluation capabilities through the gateway with integrated health monitoring across multiple transport layers.
• Model Provider Adapters - Standardizes streaming behavior and deadlines across diverse AI model providers for consistent integration.
• Prompt Templates - Creates and stores prompt templates and executes them by providing arguments.
• Semantic Chunking - Divides text into meaningful segments based on semantic boundaries and markdown headers to optimize context windows.
• Virtual Entity Exposure - Groups specific tools into a virtual entity to provide a unified endpoint for AI agents.
• AI Safety Auditing - Identifies demographic bias, detects harmful content, and scans for personally identifiable information.
• Tool Execution Visualizations - Visualizes invocation latency and success rates of AI tools to identify performance bottlenecks.
• Structural Code Parsers - Parses source code using ASTs to split files into functions, classes, and methods.
• Natural Language Data Queries - Translates natural language questions into executable pandas code for statistical analysis of CSV files.
• Entity Registries - Maintains a relational database catalog of registered entities and configuration to ensure persistence across restarts.
• Tool Result Caching - Stores tool responses and prompt-based results to reduce latency and eliminate redundant computation.
• Strict Tool Argument Validators - Enforces strict JSON Schema validation for tool arguments during registration.
• Read-Only Resource Exposure - Exposes text or binary data via unique URIs for retrieval by AI models.
• Code Context Retrieval - Retrieves real-time configurations and code snippets to provide current context for AI assistants.
• Session State Management - Tracks session ownership and event history to enable the replay and resumption of AI-driven tasks.
• Hybrid Runtimes - Utilizes a native Rust implementation for high-traffic data paths to increase request throughput over Python.
• Plugin Lifecycle Management - Handles the discovery, loading, and runtime control of both native and remote external plugins.
• Request Processing Plugins - Executes a chain of modular filters for rate limiting and response transformation during request processing.
• API Specification Importers - Creates gateway records by importing API specifications or using reflection-style discovery.
• MCP Protocol Debugging - Provides a visual interface to invoke functions and inspect JSON-RPC messages for diagnosing MCP protocol issues.
• API Traffic Management - Provides centralized control over API stability through rate-limiting, retry policies, and timeout settings.
• Traffic Control - Provides traffic control mechanisms including retry policies and circuit breakers to ensure backend stability.
• Declarative Configuration Files - Automates server registration and categorization through declarative YAML configuration files.
• Dynamic Code Safety Analysis - Analyzes Python code before execution to detect restricted operations and imports for secure sandboxing.
• API Token Management - Issues and revokes scoped API tokens with restrictions on servers, IP ranges, and usage quotas.
• Virtual Tool Composition - Groups multiple servers and tools into a single unified service endpoint for simplified access.
• Worker Process Management - Spawns multiple worker processes to bypass the GIL and utilize all available CPU cores for higher throughput.
• API Client Integrations - Provides standardized interfaces for external applications and plugins to consume federated AI tools.
• API Request Proxying - Maps tool parameters to query strings and headers to proxy requests to upstream REST APIs.
• Chat Session Managers - Tracks state and coordinates models over persistent connections for session-oriented AI interactions.
• MCP-to-Stdio Proxies - Translates remote server communication into local standard input/output streams for GUI clients.
• Networked Stdio Exposures - Translates network-based APIs into standard input/output streams for clients without network support.
• Middleware-Based Request Pipelines - Implements a modular chain of pluggable components to intercept and process network requests and responses.
• gRPC Microservices Integration - Integrates external plugins using a high-performance gRPC transport layer with secure encryption.
• Model Request Proxies - Relays requests through a unified interface to route and coordinate multiple interchangeable language model providers.
• Multi-Transport Server Architectures - Provides connectivity for tools via streaming HTTP for remote access and standard I/O for local development.
• Multi-Protocol Routing - Routes requests through a centralized catalog and dispatches them to the appropriate runtime module across different protocols.
• Transport Identification - Identifies communication protocols by analyzing server URLs or explicit configurations.
• Host Allowlisting - Uses a host allowlist to limit which upstream schemes and hostnames tools can connect to.
• Stdio to SSE Bridges - Bridges standard input/output servers to streaming events to make them accessible via HTTP.
• Circuit Breakers - Implements circuit breakers to stop traffic to failing tool servers and prevent cascading failures.
• Traffic Protocol Inspection - Displays a real-time bidirectional log of JSON-RPC messages between clients and servers for protocol inspection.
• Multi-Transport Endpoints - Routes requests across diverse communication patterns through a single unified service layer.
• Virtual Server Orchestration - Composes multiple tools into a single virtual server accessible via streaming events.
• Plugin Lifecycle Hooks - Runs custom logic before and after tool invocations via a specialized plugin framework.
• AST-Validated Sandboxes - Implements an isolated execution environment that uses AST analysis to block dangerous Python operations.
• Tool Interaction Auditing - Generates structured, immutable audit logs with identity attribution for every tool interaction.
• AI Content Filters - Integrates with moderation services like LlamaGuard to mask PII and prevent prompt injection attacks.
• Hardened Container Images - Provides a secure, non-root runtime image based on RHEL UBI for hardened deployments.
• Team-Scoped Visibility Controls - Provides multi-level resource visibility including public, team-scoped, and private access levels.
• Service Account Authorization - Uses domain-wide delegation and service accounts to secure server-to-server authentication.
• Identity Propagation - Forwards authenticated user details to upstream servers via HTTP headers to enable downstream authorization.
• SSO Integrations - Integrates with Keycloak via OpenID Connect for centralized identity and access management.
• Input Sanitization - Scans incoming data for XSS, SQL injection, and command injection patterns to prevent attacks.
• JWT Authentication - Validates user identity using symmetric or asymmetric JSON Web Tokens with audience and issuer verification.
• AI Agent Tenant Isolation - Ensures multi-tenant security through team-scoped catalogs and isolated secret stores for AI agents.
• OAuth Scope Restrictions - Limits token permissions to specific servers or API actions to enforce the principle of least privilege.
• Client Request Quotas - Enforces per-client request quotas to protect backend tool server capacity from exhaustion.
• Custom Role Definitions - Bootstraps predefined roles with specific permission sets using JSON configuration.
• SCIM Provisioning - Automates user provisioning and attribute synchronization utilizing the SCIM protocol.
• Secret Encryption - Protects tool credentials and OAuth tokens in the database using AES-GCM and Fernet encryption.
• Secret Management Integrations - Integrates with external secret stores to securely inject credentials into the system runtime.
• PII Masking - Intercepts tool invocations to mask PII such as emails and credit cards before requests reach backend servers.
• Single Sign-On Integrations - Enables enterprise identity management and single sign-on through Microsoft Entra ID integration.
• SSRF Protections - Blocks requests to internal resources and cloud metadata services using DNS validation to prevent SSRF.
• TLS Transfer Security - Configures SSL/TLS and mTLS authentication to ensure encrypted data transfer for the Model Context Protocol.
• Tool Namespace Prefixes - Groups tools into namespaces using prefixes to prevent naming collisions across federated servers.
• Backpressure Coordination - Prevents system overload by rejecting excess incoming requests with 503 responses when capacity is reached.
• Distributed State Synchronization - Synchronizes session and runtime state across distributed server instances to support multi-pod deployments.
• Remote Server Registrations - Registers reverse-proxied servers into a central catalog to be treated as native servers.
• JSON Schema Validation - Enforces strict specification compliance for tools and prompts during registration using JSON schema validation.
• Resource Registration - Associates data sources with unique URIs and MIME types for discovery and access.
• Agent Health Monitoring - Tracks protocol-specific metrics and runtime health to ensure the availability of connected AI agents.
• Application Metric Tracking - Tracks response latency, computational efficiency, and translation quality across multiple languages.
• Authentication Event Auditing - Logs authentication attempts and administrative changes to maintain a verifiable audit trail for compliance.
• Observability Data Emission - Publishes structured logs, counters, and audit events to a centralized system for comprehensive observability.
• Prometheus-Based Metric Exporters - Exposes a dedicated metrics endpoint to export system performance and request rates in Prometheus format.
• Request Correlation - Propagates unique correlation IDs through middleware to link events across a single distributed request.
• System Health Dashboards - Provides interactive dashboards with performance charts to monitor operational status and identify error-prone resources.
• Flame Graph Generators - Renders operation timings and call hierarchies using flame graphs and Gantt charts for performance analysis.
• LLM-As-A-Judge Scoring - Assesses model outputs using a high-capability language model acting as a judge based on defined rubrics.
• Response Quality Scoring - Scores generated responses using custom evaluators to measure overall quality and computational cost.
• AI Evaluation Suites - Executes comprehensive batches of quality, safety, and performance tests to generate model readiness reports.
• Pluggable Cache Backends - Supports pluggable cache backends like Redis or in-memory stores for frequently accessed authentication and tool lookups.
• Request Lifecycle Hooks - Executes custom logic during critical phases of prompt fetching, tool invocation, and resource retrieval.
• OpenAPI Publishing - Exposes a type-safe API contract as an OpenAPI-compliant REST interface for external tool discovery.

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