"Parsing and Unparsing CSV Files for AI Context Use" matching MCP servers:

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Comprehensive CSV processing MCP server with 40+ operations for data manipulation, analysis, and validation. Features auto-save, undo/redo, and handles GB+ files

This read-only MCP Server allows you to connect to CSV Files data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for CSV Files (https://www.cdata.com/drivers/csv/download/mcp).

Enables AI assistants to answer natural language questions about data in CSV files using Polars filter expressions.

Enables local, private analysis of CSV and TSV files through natural-language agents such as Grok Bot and Cursor, offering file listing, profiling, SQL-like queries, summaries, group-bys, pivot tables, cleaning previews and generated charts. All computation happens on the user's own machine against an allowlisted folder, with no network calls, telemetry or data leaving the host.

Loads CSV files into a temporary SQLite database and provides comprehensive data analysis tools via MCP, enabling AI assistants to query, analyze, and export data using natural language.

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

Manages LinkedIn prospecting CSV files with deduplication and search, reducing LLM token usage by offloading file operations.

MCP server for executing Databricks SQL queries and comparing table data, supporting CLI and VS Code integration.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Enables agents to query a local macOS archive of Knowledge and browser history through read-only stdio MCP tools for status, timelines, search, original records/blobs, schemas, bounded SQL, and usage or domain summaries.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

Indexes codebases into a SQLite database to provide metadata, exports, dependency graphs, and change tracking for JS/TS projects. It enables users to search for symbols, map internal dependencies, and monitor file changes through MCP tools and a web dashboard.

Search the fastest-growing US horse boarding directory: 6,000 facilities and 3,800 trainers across 48 states, plus boarding price data by state.

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables AI assistants to interact with generated REST APIs by discovering and using CRUD tools and read-only data resources.

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Enables agents to pull compact MySQL row diffs into their context, supporting before/after changes via triggers or watermark-based updates, with tools to fetch and acknowledge changes.

Enables MCP clients to retrieve relevant memories and save confirmed durable facts through authenticated access backed by PostgreSQL provenance and Qdrant semantic search.

Enables AI assistants to interact directly with generated REST APIs by exposing CRUD operations as MCP tools and read-only data as resources.