"IBM MCP Context Forge - Model Context Protocol Gateway Tool" matching MCP servers:

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.

Provides Chinese cultural and public data as MCP tools, including random classical poems, Chinese holiday and compensatory workday queries with annual summaries, and historical events for any date.

A system for defining, composing, and managing task-specific contexts for AI-powered development, enabling consistent, high-quality results.

Enables AI assistants to interact seamlessly with IBM watsonx.data lakehouses using natural language for operations like querying, catalog browsing, engine management, and data ingestion.

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

Provides a local SQLite-backed code context knowledge base with MCP tools for storing and querying code facts, call graphs, semantic info, evidence, and business mappings, plus versioned snapshot publishing and incremental sync.

Context+ is a semantic intelligence server that transforms codebases into searchable, hierarchical feature graphs using RAG, Tree-sitter AST, and spectral clustering. It provides tools for deep code discovery, blast radius analysis, and memory graph management for high-accuracy engineering tasks.

Provides access to ancient text corpora via stdio, enabling corpus listing, status checks, searching, and text/metadata retrieval with provenance envelopes.

Enables AI tools to operate IBM i 5250 green screens, driving and testing interactive RPG programs by seeing screens, typing into fields, and pressing keys.

Unified MCP and skill management gateway for AI agents, enabling tool discovery, installation, and sharing with 99% context token savings.

A multiplexing gateway that aggregates multiple MCP servers into a single port, significantly reducing context token usage through a Meta-MCP discovery system. It enables dynamic tool discovery and invocation across various transport protocols including stdio, HTTP, and SSE.

Aggregates multiple MCP servers into a single gateway with unified top-level tools, reducing LLM context usage and enabling IDE compatibility by consolidating many tools into fewer interface functions.

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.

Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.

This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Production-grade MCP Gateway in Go featuring dynamic OpenAPI tool registration, RBAC governance, in-flight PII/DLP redaction, and structured JSON audit logging.

This MCP server preserves a Bearer principal on every tool call and SSE frame, rotates vaulted credentials in place, and enforces per-tool allowlists for agents.

An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.