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465,824 tools. Updated 2026-08-19 07:16

"Elm" matching MCP tools:

  • Detect honeypots, rug pulls, and scams in any EVM token. Returns a risk score with flags for hidden ownership, mint authority, tax rates, and more across 40+ chains.
    MIT
  • Analyze charts, graphs, diagrams, and UI screenshots to extract insights and answer specific questions about data visualizations, flowcharts, and technical layouts.
    MIT
  • Load long text content into a session for RLM processing. Store it with a context ID to enable decomposition, search, and analysis.
    MIT
  • Obtain quick answers and explanations from GLM's cost-efficient model for brainstorming, analysis, and general questions.
    MIT
  • Extracts all visible text from images using OCR. Ideal for screenshots of documents, code, error messages, or any image where text content matters.
    MIT
  • Transcribe local audio files (m4a, mp3, wav, ogg, flac) into text with faster-whisper. Get segmented and full transcripts, optionally translated.
    MIT

Matching MCP Servers

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    Enables AI assistants to interact with IBM Engineering Lifecycle Management (ELM), including DOORS Next Generation, EWM, and ETM, for managing requirements, work items, tests, and project builds through natural language.
    MIT
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    A federated MCP server that combines IBM's engineering-ai-hub tools with its own authoring and orchestration tools for IBM ELM, enabling AI hosts to manage requirements, models, and compliance through a single interface.
    MIT

Matching MCP Connectors

  • Tesouro em Foco is a free remote MCP server that brings Brazilian government bond (Tesouro Direto) pricing into AI assistants such as Claude, Cursor, and any MCP-compatible client. The engine implements the Brazilian National Treasury's official pricing methodology — validated against 269,000+ real trades — and covers all retail bond types: fixed-rate (LTN, NTN-F), inflation-linked (NTN-B Principal, NTN-B), and retirement/education bonds (Renda+ and Educa+, NTN-B1).

  • Browse and buy ELC Conference 2026 engineering leadership tickets in Prague via AI.

  • Break long context into chunks using strategies like fixed size, lines, paragraphs, sections, regex, or sentences, allowing processing of arbitrarily long text without external APIs.
    MIT
  • Search long contexts with regex to locate relevant sections, returning matches with surrounding context and line numbers for quick review.
    MIT
  • Set or update the answer for the current task, storing partial or complete content. Mark answers as ready when final for later retrieval.
    MIT
  • Create a new isolated RLM session to run independent processing contexts. Use it when you need multiple separate workspaces or parallel tasks without interference.
    MIT
  • Explain code or concepts by providing a snippet or topic with optional context, and receive clear, actionable explanations for learning, documentation, and deeper understanding using GLM.
    MIT
  • Analyze codebases to understand structure, find patterns, map dependencies, and assess architecture using read-only access.
    MIT
  • Handles implementation tasks by writing code, creating files, making changes, and refactoring directly in your project directory with full write and edit access.
    MIT
  • Generate text descriptions, captions, or alt text for images. Choose brief, detailed, or exhaustive detail to get the summary you need.
    MIT
  • Retrieve the confirmed and pending nonce for any EVM wallet address. Use the pending nonce to build new transactions on Ethereum, Base, Polygon, Arbitrum, and Optimism.
    MIT
  • Retrieve Japanese legal statutes from the e-Gov API. Get full law content or summaries for large documents, with JSON or XML formatting options.
    MIT
  • Analyzes images from URLs or base64 data, returning structured JSON with summary, text, and detected objects for one-time image analysis.
    MIT
  • Engage in multimodal, multi-turn conversations that include images, with concise automatic replies. Ideal for interactive image discussions instead of structured analysis.
    MIT
  • Retrieve the actual content of specific chunks from a decomposed context, enabling targeted processing after segmentation.
    MIT