"Using llama.cpp and Searxng with local models" matching MCP servers:

Local-first MCP server for Power BI Desktop automation. Automate semantic model changes, DAX, Power Query, Excel, and report layout from MCP-capable AI clients.

Enables AI assistants to programmatically read, modify, and query Tableau workbook files (.twb and .twbx) on the local filesystem via 80 tools.

Enables users to inspect local Hyper files by listing files, schemas, and tables, previewing up to 100 rows, and generating sample sales Hyper data.

Enables rendering interactive UI widgets and data visualizations for Claude.ai through MCP, supporting callback-based forms and client-side charts.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

Enables AI clients to inspect and modify maps and scenes open in a locally running desktop GIS session through natural language, exposing 38 fixed tools for session and layer queries, fields, selection, visibility and renaming, symbology, transparency, camera and elevation, map switching, raster, layout export, whitelisted geoprocessing, and attribute editing. It routes stdio MCP calls through a Windows named pipe to a C# add-in, with no network ports and no arbitrary code execution.

Enables Codex to control a Gephi 3D network view through natural language, including network generation, 3D layout, node focus and neighbor highlighting, layered views, and PNG/PDF export.

Enables Claude to interact with local workspace files through MCP, including listing files, summarizing CSV datasets, generating plots, executing Python scripts, and running allowlisted shell commands from a chat interface.

Enables AI assistants to discover and interact with local Power BI Desktop instances, including inspecting table/column schema and executing DAX queries.

Generates beautiful Excalidraw diagrams from natural language descriptions using a local llama.cpp LLM, entirely offline.

Enables natural-language queries about sales metrics, returning interactive charts inline in chat via MCP Apps with a markdown fallback for unsupported hosts.

Options analytics an agent can drive: import a permitted chain, solve implied volatility, build a sixteen-Greek ladder, map dealer gamma walls, rank twenty-three structures, run a GARCH-t simulation with a convergence gate, and backtest with modelled premiums. Twelve stdio tools; every output is a schema-validated JSON file with source, assumptions and a degraded flag. Research only, no orders.

Enables rendering, validation, formatting, and inspection of D2 diagrams via the Model Context Protocol, using WASM for rendering without requiring the d2 binary.

Renders OHLCV data as candlestick chart PNGs and returns inline images. It is a pure renderer with no network access, allowing models to view charts generated from provided data.

MCP server for AI Diagram Maker — generate software engineering diagrams from natural language, code, ASCII diagram, images, or Mermaid. Inline diagram rendering using MCP apps UI and diagram URL in responses. Works with Cursor, Claude Desktop, Claude Code, and any MCP-compatible AI.

Enables LLMs to generate visual summary reports from CSV datasets using MCP Resources, Tools, and Prompts, without exposing raw data to the model.

Generates and downloads charts using QuickChart.io through Claude Desktop and other MCP clients.

An MCP server that lets Claude or ChatGPT read, create, and edit mind maps stored in a GitHub repository, with support for local and HTTP transport.

MCP server that enables natural-language-driven, headless HEC-RAS modeling, letting agents inspect, edit, run, compare, and plot 1D/2D hydraulic models without the GUI.

MCP server for generating diagrams, charts, and visualizations using Gemini image generation on Vertex AI. Supports auto-detection of diagram types, multiple style modes, and iterative refinement.