"Information about 'Terminal'" matching MCP servers:

MCP server that provides a live, terminal-native layered dependency map for AI coding assistants, with tools to track and visualize development progress in real time.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Gives terminal coding agents a local browser canvas for rendering charts, tables, metrics, markdown, live widgets and sandboxed MCP App UIs that would otherwise reach them as raw JSON. Lets agents pose blocking visual multiple-choice questions and read back the user's clicks, selections and an animated architecture map of file activity.

Enables AI assistants to understand and analyze Spring Boot codebases through static analysis, allowing natural language queries about endpoints, classes, dependencies, and architecture without running the application.

Enables AI agents to create, validate, and manage buildingSMART IDS (Information Delivery Specification) files with 100% compliance to the IDS 1.0 standard using IfcOpenShell's IfcTester library.

Semantic React/TSX analysis MCP server that saves 70-90% tokens by using AST to retrieve precise component usages, prop flow, and state update information for AI coding assistants.

Plan your projects with The High Council! Several AI models from different labs argue about your idea, then debate on Software Engineering choices. The goal is to get everyone to work together, in harmony and with manners. Effectiveness is unproven, but it is a fun way to spend your tokens!

Enables MCP clients such as Claude Code, ChatGPT desktop Codex mode and Codex CLI to declare and maintain a live, terminal-native layered dependency map of a system as it is built. Exposes tools that lay down a ghost design first, mark the node currently under construction, and light nodes solid only when they are built and verified.

Enables AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

Enables coding agents to declare an entire system design up front as ghost nodes, then light each node up as it is built and test-verified, exposing a live layered dependency graph in a terminal pane beside the conversation. Structural corruption such as upward-pointing edges is refused, while progress and broken foundations stay visible through eight mmap_* tools.

Exposes eight mmap_* tools that let a coding agent declare a whole layered dependency design as ghost nodes, then light nodes through building and verification while a live terminal pane shows the map. The server also tracks status such as in-progress spinners, verified-green nodes, and red cracked foundations.

Enables managing Cacoo diagrams, folders, organizations, and account information through MCP tools over HTTP, with OAuth-based authentication, email allowlisting, and support for multiple Cacoo accounts.

Lets an AI coding agent declare a system's layered dependency graph up front as ghost nodes, then light each node up as code is built and verified, with a live terminal pane showing spinners on active work and red on cracked foundations. It exposes eight mmap_* tools so structural rules — downward-only edges and unique ranks — are enforced as the design ledger.

Enables an AI coding agent to declare a layered dependency structure as ghost nodes and then turn those nodes solid as they are built and verified, keeping the design, current attention, and any cracked foundations visible in a live terminal pane.

Enables an AI coding agent to publish a live, layered dependency map of a system under construction: it ghosts the intended structure first, then lights nodes up as they are built and verified. A terminal pane beside the conversation keeps that map visible, showing what is being worked on now and flagging when later changes crack a foundation beneath it.

Retrieves architectural information from ArchiMate models, enabling AI coding assistants to access architectural context during the software development lifecycle. Supports search and retrieval of views and elements in markdown, JSON, or YAML.

Publishes eight tools that let an AI coding agent declare an entire layered dependency design as ghost nodes before writing code, then light each node up as it is built and verified—with a spinner on the current node and reddening when later work cracks a foundation. This gives developers a live, terminal-side view of bottom-up progress where upward edges and duplicate ranks are refused, so a bad design can be vetoed while it is still only a picture.

Enables coding agents to declare a layered dependency map as ghost nodes and then light nodes up as they are built and verified, with a live terminal pane and eight tools for tracking structure during bottom-up development. It keeps edges pointing downward, shows what is being worked on with a spinner, and marks cracked foundations red.

Builds a dependency and topology graph of a Kubernetes/FluxCD GitOps repository via static analysis, enabling AI agents to reason about service relationships without cluster access.

Enables coding agents to publish a layered dependency map of a system as ghost nodes, then light each node up as it is built and verified, with cracks and faults propagating visibly to upper layers. It exposes tools for declaring ranks, edges and evidence so a terminal pane beside the session always shows current structure and progress.