"A tool for generating data visualizations and charts" matching MCP servers:

A modern Python MCP server for rendering customizable charts (line, bar, pie, scatter, area, combined dashboards) with Plotly, supporting export to PNG, SVG, and base64, and terminal charts with ANSI output.

A Model Context Protocol server that generates lightweight ASCII charts directly in terminal environments, supporting line charts, bar charts, scatter plots, histograms, and sparklines without GUI dependencies.

An MCP server that enables AI assistants to programmatically create and manage flowcharts, ERDs, and swimlane diagrams. It provides tools for manipulating diagram structures, performing auto-layouts, and exporting to Mermaid or Markdown formats.

Enables LLMs to perform addition calculations through a stdio-based MCP server with an add_numbers tool and type-safe validation.

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

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.

A Model Context Protocol server that provides a scratchpad tool for LLMs to organize thoughts, take notes, and plan approaches to complex problems.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

Provides a unified MCP server to interact with multiple middleware (Loki, MySQL, Redis, RocketMQ, Elasticsearch, MongoDB) through a desktop application for connection management and configuration export.

Enables converting documents between formats over MCP: any supported input (docx, odt, xlsx, ods, pptx, odp, pdf) to structured Markdown, Markdown to docx, odt or pdf, direct cross-format pairs, and Markdown to sanitised HTML. Fidelity losses are reported rather than failing the conversion.

An MCP server that provides utility tools for text analysis, URL metadata fetching, and temperature conversion.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables document conversion between PDF, DOCX, and Markdown formats to facilitate reading and editing complex files in AI tools like Claude Desktop or Cursor. It utilizes marker-pdf and pandoc to provide structured text versions of documents, helping to manage context and support unsupported file types.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

TableCharts MCP Server gives any LLM assistant the ability to instantly convert tabular data into beautiful, hosted, interactive dashboards. Simply provide JSON rows, raw CSV text, or a public URL (Notion page, Google Sheet, or Salesforce report), and get back a shareable dashboard URL plus an embeddable iframe code — all in one tool call. Supports 6 chart types: bar, line, area, pie, scatter, a

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

Enables Claude to search a THING API for matching items and return top results with names and ids, handling empty results and API errors cleanly.