"IBM MCP Context Forge - Model Context Protocol Framework" matching MCP servers:

Sovereign, deterministic, local-first context and DAG lifecycle engine with native stdio MCP server for autonomous AI coding agents.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

Share context, detect file conflicts, and send messages across parallel Claude Code sessions

Reduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

A local-first bridge for moving useful context between coding agents, indexing sessions and creating handoff packets for seamless agent switching.

MCP server to provide Jira Tickets information to AI coding agents like Cursor

a claude code harness engineering tool

Prototype MCP server enabling coding agents to manage Amigo Agent Forge operations, including org credentials, entity configurations, conversation simulations, and version sets.

Provides MCP tools for AI-powered code review, research, and book writing via a fleet of coding agents.

MCP server that lets Claude Code, Codex or opencode hand coding work to cheaper models run by opencode.

Enables coding agents to incrementally index project text and code, persist decisions and constraints with clear sources, and assemble focused project context across sessions via MCP.

Enables AI agents to investigate and repair Python/pytest repositories in isolated Git worktrees with audit trails, without modifying the original repository.

This MCP server enables automated maintenance and code analysis for Python/pytest repositories in isolated Docker environments. It supports read-only investigations, fix-and-verify tasks, and provides full audit trails with SQLite event history and artifact exports.

Enables bounded, local coding tasks to be delegated to OpenAI-compatible models with explicit working directory and read/write paths, pre-application unified-diff validation, and optional verification commands. It routes tasks by complexity and risk, while blocking sensitive changes such as auth, payment, secrets, migrations, database, deployment, or production environments.

Enables coding agents to check human-stated assumptions against authoritative sources like AWS AppConfig, Sentry, and the repository, detecting when beliefs have drifted from reality. It provides tools to inspect belief history, list verifiable properties, review open drifts, and record verified understanding.

Generates project features (HTML pages, backend APIs) from prompts and project-specific business rules stored as resources, with support for interchangeable LLM providers (local Ollama or cloud Gemini).

Provides tools for Cursor to interact with IBM Bob Shell, enabling Bob-powered coding assistance, session management, and task delegation directly from Cursor.

Enables AI coding agents to save task context as a validated JSON document and later restore it as a concise briefing with the objective, status, relevant files and focus lines, decisions, and prioritized next steps.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.