Ecosystem Intelligence MCP
Provides bounded, read-only analysis of local project directories, enabling inventory, marker detection, and safe file reads without executing code.
README
Ecosystem Intelligence MCP
A sellable prototype for bounded, read-only understanding of local project directories. It is designed to help an AI assistant inventory projects, identify technology markers, and read selected text files without executing code or exposing arbitrary filesystem paths.
Product documents
- What This Is
- What This Can Do
- Changelog
- Advanced Handoff
- Build and Review Record
- Next Phase Specification
- Session Checkpoint
Tools
inventory_roots— shows the configured allowlist.summarize_project— bounded directory structure, file counts, extensions, markers, and representative text-file paths.read_project_file— reads a small recognized text/project file under an approved root with sensitive-path filtering and redaction.analyze_project— infers purpose and capabilities from bounded evidence, with confidence, readiness, unknowns, and safety metadata.
It also exposes ecosystem://policy as a resource describing the safety model.
Run locally
npm install
ECOSYSTEM_ROOTS="$HOME/Documents" npm start
The server communicates over MCP stdio. It does not execute project code, mutate files, follow symlink escapes, inspect unconfigured roots, or read arbitrary binary files. Common sensitive paths are excluded and common secret patterns are redacted.
Package direction
manifest.json is MCPB-ready for a future downloadable bundle. Before selling, add signed release artifacts, a privacy policy, support documentation, a second platform test, and a hosted/paid tier only after validating demand.
Product boundary
The commercial moat should be the interpretation layer: project-purpose extraction, capability overlap, maturity scoring, and actionable productization recommendations. Keep those higher-level features separate from the local filesystem adapter so the product can later support uploaded manifests, Git repositories, or a hosted service.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.