"A messaging and communication platform" matching MCP servers:

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

Enables reading and building Splunk dashboards via the management REST API, using SSO for permissions and supporting both Studio and Classic formats.

An HTTP MCP server for exploring CSV files that masks PII before any code execution, enabling safe data analysis with local or cloud LLMs.

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.

Renders word-sized charts — sparklines, bars, bullets and 100+ more types — to self-contained SVG with generated alt text, small enough to sit inline in a model's reply or a table cell. Three tools: find the right chart for a question, get its wiring, and render it.

A read-only, cross-platform visualization layer for Cortex that renders memory, sessions, and code into six live reading angles (graph galaxy, 3D anatomical brain, execution trace, consolidation board, knowledge browser, and wiki) without ever writing a memory.

Computes biorhythm values (physical, emotional, intellectual) and generates a PNG chart showing sine curves based on birth date and optional target date.

Visualizes conversations as structured hierarchical mind maps by injecting a prompt that instructs the LLM to generate a JSON mind map — no external APIs required.

A review-only MCP server that provides tools for reviewing trade records against a live TradingView Desktop chart over Chrome DevTools Protocol on localhost.

A production-grade MCP server that exposes Tableau Server/Cloud as a BI platform, enabling project, workbook, data source, user, group, job, lineage, and export operations via natural language, with role-based permissions and token optimization.

Connects AI clients to the Matih data platform, enabling SQL queries, table profiling, analytics, chart creation, and file uploads through MCP tools with OAuth/PKCE authentication.

Provides advanced charting widgets (uPlot time series, ECharts box plots and parallel coordinates) for Urano Desktop/Cloud via MCP tools that build uiSpec JSON for rendering.

Enables Claude Code and other processes to control the lys interactive multi-dimensional data analysis and visualization platform by executing Python commands in it and viewing the resulting lys and matplotlib graphs. It connects over a local socket or TCP to a lys instance running in remote mode.

Enables AI assistants and teams to collaborate in a shared workspace by building and querying tables, writing documents, uploading files, and publishing live dashboards through any MCP-compatible client.

Enables AI clients to render a workspace from a table by selecting an appropriate widget and DataProfile based on a user intent, then returning a chart view with a signed data link.

Exposes tools and resources for human-supervised geological modelling from borehole data, including validation, 3D implicit model construction with GemPy, and uncertainty analysis, with a deterministic audit trail.

A TypeScript/Node.js MCP server that wraps the Metabase REST API, enabling AI agents to execute queries, explore schemas, and build dashboards through structured tool calls.

Enables conversational patent data analysis through a set of tools for trend analysis, clustering, word cloud generation, and more. Allows natural language queries to explore patent datasets with structured results and visualizations.

A data visualization MCP server built on FastMCP, providing 32 tools for chart generation, report building, and data processing.

MCP server for controlling jamovi from MCP clients. It starts a local jamovi engine process and exposes tools for opening datasets, reading/writing data, running analyses, exporting results, and saving .omv files.