"Information about MCP Discovery" matching MCP servers:

Enables users to start from a seed host, traverse CDP/LLDP neighbor evidence and device information over SSH, and produce an editable Draw.io topology diagram plus supporting evidence for engineer review. It supports explicit host/CIDR scoping, bounded depth and device counts, strict host-key checks, and a read-only command policy, with an offline simulated demo.

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

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.

Enables AI assistants to check server status, inspect Camunda Hub API v2 connection information, and read Hub files by UUID, with planned support for prompt-driven BPMN diagram creation, editing, validation, and visual review.

Enables agents to analyze codebases (local or GitHub) and automatically generate Mermaid diagrams rendered as PNG images via Kroki, providing visual understanding of project structure and flow through file discovery, reading, and diagram generation.

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.

A centralized remote MCP server that gives AI coding assistants real-time microservices architecture intelligence, including service search, database schema lookup, API discovery, and change blast-radius analysis.

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.

Enables source-backed discovery of application purpose, architecture dependencies, field usages, and SQL transformations and value lineage. It provides cited evidence, separates confirmed facts from interpretations, and exposes coverage gaps, with optional offline local-model reasoning.

Enables AI coding agents to understand unfamiliar codebases through project summaries, file explanations, feature tracing, change planning, test discovery, guided walkthroughs, and token-efficient repository digests.

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.

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 cross-repository architecture discovery and dependency routing across multi-service codebases, with automated scanning and batch AST indexing for AI agents to trace request lifecycles and navigate service boundaries.

Enables AI assistants to programmatically create and manage information flow diagrams — information items and information flows in StarUML.

Enables scanning Java/Maven projects to generate TOGAF-standard architecture deliverables (Excel and PPTX) via natural language commands.

Enables analysis of Java call graphs in Axelor projects via ASM bytecode analysis, supporting dependency discovery, change impact analysis, and call chain tracing through SQLite queries.