"A tool for processing and extracting data from PDFs into structured formats" matching MCP servers:

Enables AI agents to index and search across SQLite databases and CSV files to discover table schemas and column metadata. It provides a unified MCP API for data source management and structural exploration through natural language.

Provides AI agents with data validation, transformation, and normalization capabilities, including JSON schema validation, CSV processing, data normalization, text cleaning, and dataset merging.

Enables AI agents to compute and apply minimal JSON Patch (RFC 6902) operational diffs for atomic state synchronization between structured documents. Runs as a zero-dependency Python MCP server with deterministic outputs and sub-millisecond execution overhead.

Enables autonomous agents to compute and atomically apply minimal JSON Patch (RFC 6902) operational diffs for structured data state synchronization. Runs as a zero-dependency Python MCP server supporting deterministic patching, caching, and rate-limiting workflows.

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 FastMCP server for data processing tasks including CSV, JSON, text analysis, and numeric statistics, enabling users to parse, summarise, filter, convert, and analyze data through MCP tools.

Enables agents to deterministically extract structured entities from input payloads and validate or coerce them against JSON schemas, returning a standardized response dossier with execution telemetry. It runs as a zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP clients.

This server enables multi-agent conversations for interacting with Cirro's biological data platform through its OpenAPI interface, auto-generated using AG2's MCP builder.

Enables deterministic extraction and validation of structured JSON data from inputs using a defined schema. It plugs into MCP-compatible clients like Claude Desktop, Cursor, and Windsurf to return standardized, schema-validated results.

Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

MCP server that unifies real-time telemetry from industrial systems into a single queryable interface, enabling production visibility, anomaly detection, and operational insights.

An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.

Bootstrap MCP server for future data exploration and querying across multiple databases, currently only provides a hello_world tool with no actual data connectivity.

A Model Context Protocol server for data wrangling that provides standardized interfaces for data preprocessing, transformation, and analysis tasks including data aggregation and descriptive statistics.

A FastMCP-based server that provides data analysis tools for processing, analyzing, and visualizing data with an intuitive Streamlit web interface.

Provides a read-only MCP interface to locally synchronized Amazon seller data, enabling AI agents and analytics tools to query order, inventory, advertising, and settlement information with completeness and freshness checks for safe analysis.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

Lets MCP clients safely investigate live business data across multiple enterprise APIs by giving each subject a consistent scoped pseudonym and surfacing disagreements between sources as findings.