"A simple memory MCP server with read/write tools and vector database search" matching MCP servers:

Enables natural-language interaction with Ladybug Tools for building performance simulation, 3D modeling, and data visualization, allowing agents to perform complex environmental analysis tasks without CAD software.

MCP server for rendering Mermaid diagrams and generating source-code diagrams, with tools to render text/files and produce dependency/class diagrams.

Provides honest A/B test verdicts using interactive cards, with statistical calculations for conversion rates, sample sizes, and Bayesian probability-to-beat.

A secure multi-database MCP server supporting MySQL, PostgreSQL, and SQLite with read-only enforcement, SQL injection prevention, and tools for schema analysis, performance optimization, and visualization.

Connects Google Search Console to AI assistants, enabling SEO data analysis through natural language conversations.

Enables searching Google Scholar papers with year filtering, deduplication, and proxy support. Also provides paper detail retrieval, TF-IDF relevance analysis, and chart generation via MCP tools.

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.

Integrates with Google Search Console to enable querying search analytics, comparing performance periods, generating visual reports, and identifying SEO optimization opportunities through natural language.

Enables data analysis and visualization operations such as loading CSV/Excel/JSON files, computing summary statistics, generating charts, and exploring datasets via SSE/HTTP.

Exposes semiconductor wafer analysis tools including wafer maps, P-charts, and statistical plots, enabling AI assistants to visualize and analyze wafer test data.

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

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

Bridges AI assistants to StarRocks clusters so they can execute read and write SQL, browse databases, tables and schemas, pull system metrics, and generate table/database overviews or Plotly charts from query results. Connection and behavior are configured through environment variables or a connection URL, with in-memory caching of overviews.

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.

Validates and renders declarative JSON diagram specs to themeable SVG. The format has no coordinates, so a model writes structure and the renderer does the layout; tools: validate, render, kinds.

Enables read-only analysis of FST waveform files, including signal value queries, timing measurements, event search, latency analysis, and PNG plot generation using human-readable time addressing.

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.

Enables querying Tableau lineage through the Metadata API via MCP, supporting impact analysis, where-used lookups, and content search. Provides read-only tools for answering natural-language questions about workbooks, datasources, fields, and dependencies.

A demo MCP server that returns an interactive dashboard (bar chart with dataset selector) as an MCP App via the official MCP Apps extension (SEP-1865).

Enables natural language querying of brain volumes (NIfTI) with a fixed set of tools, returning visualizations and reproducible nilearn code.