"Multi-Agent Systems with Shared Memory Storage" matching MCP servers:

Enables AI applications to interact with decentralized storage through a standardized Model Context Protocol interface, allowing file uploads, retrievals, and identity management.

Read-only MCP server for extracting data across multiple CallTrackingMetrics sub-accounts using a single parent API key. Enables cross-account call data, reports, numbers, contacts, and configuration analysis for resellers and agencies via Claude.

An autonomous MCP server that fetches datasets from IPFS/Filecoin, performs computation (anomaly detection, statistics, data quality scoring), and stores results via Multi-Chain Storage simulation.

Enables AI agents to discover and query enterprise knowledge documents and business metrics through standardized MCP tools, supporting retrieval, structured sales/order analysis, anomaly scanning, and data lineage lookups.

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

Provides deterministic tools for transforming, formatting, and inspecting structured data for AI agents.

MCP server that enables one-click BIM audits by chaining IFC statistics, validation, and quantity takeoff workflows through natural language queries.

Enables querying and mutating data across multiple sources (CSV, Google Sheets, SQLite, PostgreSQL, MySQL, MongoDB, Oracle) through a dynamic GraphQL API, with flexible filtering, batching, persisted queries, and dynamic source switching.

An MCP server that exposes Estuary Flow pipeline operations as typed tools, enabling agents to inspect pipeline health, publish captures and materializations, edit/restart tasks, and read landed documents via tool calls.

Enables agents to answer analytics questions with provable correctness by resolving metrics through the semantic layer, tracing lineage, traversing knowledge graphs, checking freshness, and searching glossary definitions, while enforcing access controls and logging every call.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Enables natural language interaction with JSON files, providing tools to load, query, aggregate, transform, and export data directly from AI editors.

Enables AI agents and MCP clients to heuristically parse multi-column document dumps, aligned spacing, and CSV/TSV input, converting them into clean GitHub Flavored Markdown tables and typed JSON. Runs fully offline on the Python standard library with zero external dependencies, returning deterministic results without LLM round-trips.

MCP server for cleaning messy CSV data via a single clean_csv tool. Uses an agentic workflow with rule-based or LLM planners to transform data safely.

Transforms high-level business intents into structured Data Product Requirement Prompts through AI-powered conversational refinement. Guides users through clarifying questions to gather comprehensive requirements for automated Business Intelligence dashboard generation.

A data governance agent that audits PostgreSQL databases through controlled MCP tools for schema inspection, null profiling, and anomaly detection.

Middleware that converts existing APIs/DBs into MCP tools with secure credential injection, data scoping, and immutable audit logs.

MCP server that parses legacy relational database schemas (Oracle, DB2, MySQL, MSSQL) and transpiles them to PostgreSQL with generated Prisma schema and TypeScript query helpers.

Enables verifiable business intelligence over federated data sources, supporting semantic queries, drill-down to source rows, and raw SQL with explicit verification status.

Enables analysis of raw SEO data by ingesting nested JSON datasets and converting them into clean, actionable metrics through the Model Context Protocol.