"Resources for accessing on-chain blockchain data" matching MCP servers:

Web scraper and token compressor that converts HTML to clean markdown with 70-80% fewer tokens. Single-page compression and multi-page BFS crawling with auto-fallback fetch modes.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

Non-custodial agent wallet with a transaction preflight that decodes an unsigned EVM tx and flags drain patterns (unlimited/large approval, approve-all, token & NFT transferFrom, proxy upgrade, on-chain permit, approvals hidden in multicall) before signing.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

MCP server that lets AI agents query live DigiByte blockchain data: network health, DigiDollar stats, oracle status, prices, address balances, transactions, and blocks.

MCP server that exposes tools for monitoring supply chain disruptions, including vessel positions, port weather, congestion, and news. Includes an AI agent that synthesizes these sources to assess route risks.

Enables minting canary markers into package-generated synthetic datasets and scanning text you control to detect when a marker released to one recipient reappears elsewhere. Produces a payload-free, hash-linked evidence chain whose export verifies offline.

Read-only MCP server for a local-first supply chain knowledge base, enabling knowledge search with source evidence, internal system troubleshooting, vendor documentation fallback, vessel tracking, and work handoff preparation.

Enables natural-language supply-chain analysis over synthetic datasets through governed SQL/Python tools, providing supplier scorecards, forecast accuracy, risk detection, landed-cost comparisons, capacity planning, and quality trend insights without the LLM performing quantitative calculations.

Enables AI agents to fetch crypto market and on-chain data, including prices across venues, token facts, OHLC candles, market overview, DeFi TVL, and wallet balances, with two free tools and the rest paid per call in USDC on Base via x402.

MCP server for Apple Notes enabling read, search, write, and sync of notes with high fidelity, including styles, attachments, and bidirectional Markdown sync.

An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Enables ChatGPT to access approved local Windows folders, run commands, control the desktop, and manage session history through a permission-bounded MCP server.

Enables ChatGPT's web interface to operate a real Mac by executing shell commands and reading or writing files over a reverse SSH tunnel, without installing a client or consuming API quota.

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

MCP server that exposes the OBBBA No Tax on Tips federal deduction logic as callable tools, enabling users to check eligibility and estimate tip deductions.

MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.

Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.