Working with a blockchain today still looks like this: open an explorer, find a transaction, switch to your wallet, manually call a contract, check the status in another tab. Every action is a separate service, a separate interface, a loss of context. That’s fine for occasional use but brutal when you’re working with the network every single day.
That’s why we wanted to make life a little easier for developers and built Monad MCP.
What is it?
Monad MCP is a tool that brings all Monad network interactions into one place. It works both through an AI agent and directly from the terminal, covering the full basic blockchain workflow no tab-switching required.
With it, you can send transactions, interact with smart contracts, check balances and operation statuses, and query block and address data all through a single interface where AI acts as a convenient layer between the developer and the network.
Who is it for?
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Builders and developers who work with Monad on a regular basis
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Teams building agentic systems on top of the blockchain who want a ready-made tool without writing custom wrappers
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Researchers who need fast access to on-chain data
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Teams integrating Monad into their infrastructure
Security
This deserves its own section.
Your private key never leaves your device. Monad MCP can prepare a transaction and pass the data further down the pipeline, but signing happens exclusively locally. Neither the model nor any remote server ever gets access to your key. Ever.
This is a core architectural decision not a setting.
Bottom line
Monad MCP is not another wallet and not a replacement for an explorer. It’s a working tool for anyone who wants to move faster on Monad without giving up control over their assets.
GitHub: https://github.com/stakeme-team/monad-agent-kit
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This is great! Thank you very much, very useful for to accelerate the development of tools and services for Monad!
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Thanks for the feedback! Yeah, it’s a really convenient tool just takes a bit of time to set up, and I guarantee it will dramatically simplify your workflow. You’ll be able to manage everything blockchain-related from a single terminal or bots that works best for you.
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I also want to share with you one skill, that will help you to find the best engineering solution:
Affiliation: I am the creator of this skill/module.
Project Link:
https://github.com/Antropocosmist/useful-skills/blob/main/triz-engineering-solver.md
What it is: The TRIZ Engineering Solver is a systematic analytical framework designed for AI Agents. It moves away from “hallucinated brainstorming” toward Genrich Altshuller’s algorithmic approach to innovation.
Technical Breakdown & Lessons Learned
The Approach: The core challenge was translating the “Contradiction Matrix” and “40 Inventive Principles” into a logic flow that an LLM can execute without losing technical rigor. Instead of just asking the AI to “be creative,” this skill enforces a 5-step constraint-based reasoning process:
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IFR (Ideal Final Result) Anchor: Forces the model to define the solution in terms of functions, not objects, which breaks functional fixedness.
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Technical Contradiction Mapping: The agent must explicitly identify which parameter (out of 39 standard TRIZ parameters) is being improved and which is being degraded.
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Matrix Logic: It uses the identified pair to pull specific principles (e.g., Principle 15: Dynamicity or Principle 10: Preliminary Action).
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Su-Field Analysis: A substance-field model is used to check if the system needs a new “field” (energy) or “substance” to resolve the conflict.
Benchmarks & Observations: During testing on classic engineering paradoxes (e.g., increasing the strength of a wing while decreasing its weight), I found that the AI’s success rate in finding “Level 3” inventions (solutions outside the immediate industry) increased significantly compared to zero-shot prompting. Without this framework, the AI tends to suggest basic material swaps; with it, it suggests structural changes like “Segmentation” or “Phase Transitions.”
Limitations:
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Parameter Mapping: LLMs still occasionally struggle to map complex physical problems to the exact 39 TRIZ parameters. Manual oversight is recommended during the mapping stage.
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Abstraction Gap: The skill provides “Principles” (e.g., “The Anti-Weight Principle”). It still requires a human engineer or a highly specialized agent to translate that abstraction into a specific CAD or material choice.
Lessons Learned: The biggest takeaway was that “Creativity” in AI is often just the result of well-defined constraints. By narrowing the AI’s focus to specific TRIZ patterns, the output becomes more “inventive” because the path of least resistance (clichés) is blocked by the methodology.
Documentation: Detailed logic and prompt structures are available in the GitHub repo linked above. Open to feedback on how to better automate the Su-Field analysis components!
