MCP Input Analyzer

@Sumedh1599

Basic information

About MCP Input Analyzer

Analyzes user-described build features (e.g. database, API integration, tools) and extracts core server requirements like resources, tools, prompts, external systems, and transports needed for MCP.

#open-source #machine-learning #library #ai#natural-language-to#mcp-compliant-input#validation-of-suppor#claude-readable-json#fallback-logic-for-u

MCP Input Analyzer config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

MCP Input Analyzer tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is mcp_input_analyzer?

mcp_input_analyzer is an open-source Python library that analyzes user‑described build features (databases, API integrations, tools) and extracts core server requirements—such as resources, tools, prompts, external systems, and transports—for MCP (Microservices Configuration Platform). It converts free‑form text descriptions into structured, MCP‑compliant input definitions.

How to use mcp_input_analyzer?

Install via pip: pip install mcp-input-analyzer (requires Python 3.6+). Import the MCPAnalyzer class, initialize it with a description string and optional fallback_behavior parameter, then call methods like extract_requirements(), generate_mcp_structure(), or create_claude_json() to obtain the desired output.

Key features of mcp_input_analyzer

Natural language to structured build feature extraction
MCP‑compliant input structure generation
Validation of supported tools and protocols
Claude‑readable JSON definition creation
Fallback logic for unsupported build features

Use cases of mcp_input_analyzer

Automatically generating MCP server configurations from feature descriptions
Translating team requirements into structured dependency definitions
Creating Claude‑compatible JSON inputs for downstream processing
Validating whether specified tools and protocols are supported

Frequently asked questions

What Python version is required?

Python 3.6 or higher is required.

How do I install mcp_input_analyzer?

Install via pip: `pip install mcp-input-analyzer`. Alternatively, clone the repository and run `pip install .` from the source directory.

What does the library output?

It can output a dictionary of structured server requirements (`extract_requirements()`), an MCP‑compliant input structure (`generate_mcp_structure()`), or a Claude‑readable JSON definition (`create_claude_json()`).

Does mcp_input_analyzer handle unsupported features?

Yes, it includes fallback logic. The `MCPAnalyzer` constructor accepts a `fallback_behavior` parameter (default or custom) to control how unsupported features are handled.

Is mcp_input_analyzer production‑ready?

No. The library is currently in early development and some tests may be failing. Contributions to fix issues are welcome.

Comments

More Developer Tools MCP servers

Cronwatch

cronwatchdev

CronWatch records every run of your scheduled jobs in your own database, and speaks up when one is missed, fails, gets stuck, runs slow or costs too much. CronWatch is free and open-source.

GripForge

gripforgeai

The tools your AI needs to make your games. Turn prompts into production-ready game assets — animated characters with their weapons attached, seamless textures, terrain, VFX, HUDs — and playable game kits Unity, Godot, U

OrangePro

Aamir Siddiqui

OrangePro local-first CLI + MCP server for behavior mapping, grounded test generation, and dynamic proof.

FARPY

mangomunchr

Verified distributed compute for humans, applications, and agents. Submit Blender, Octane, and video workloads, route them across qualified GPUs, track asynchronous execution, download finished artifacts, and verify FARP

Launchdog

Devin Starks

Launch your product from your AI agent. Connect Claude Code, Codex, Cursor or ChatGPT to Launchdog over MCP and your agent drafts the listing, picks a launch week and books it. 50 launches a week, ranked only by human vo