"Multi-agent AI systems for collaborative code development" matching MCP servers:

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.

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.

A fast, safe MCP server for YNAB providing read-only budget access by default, with 28 tools for budgets, transactions, categories, payees, accounts, scheduled transactions, and server-computed analytics.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

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.

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

Enables Alexa+ to answer conversational weather queries by retrieving current conditions, forecasts, alerts, and historical data from Open-Meteo, NWS, and simulated IoT sensors stored in SQLite.

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.

Enables governed interaction with synthetic people data through MCP tools, providing per-caller scoped access, semantic-layer metrics, and read-only SQL with authorization checks.

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

Enables natural language-powered ETL workflows using Airflow, AWS Glue, Athena, and S3, allowing LLM agents to control and monitor data infrastructure.

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-assisted CSV data quality profiling through MCP tools, offering analysis reports and quality scores while preserving privacy by processing only aggregated data.

Exposes a DuckDB-backed MCP server that lets an agent fetch a dataset's schema and run read-only SQL queries against it. All execution is filtered server-side, enforcing single-statement, read-only safety independent of the calling model's behavior.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Enables autonomous agents and scraping pipelines to deduplicate high-throughput event streams, repair and validate gigabyte-scale JSONL data, retry HTTP/S requests with exponential backoff and header rotation, mimic Chrome/Safari TLS fingerprints, and sync cross-domain cookie sessions. Tool invocations are metered through x402 USDC micropayments on Base L2, returning a payment challenge until a signed transaction unlocks execution.

It gives Claude Code, Cursor, and Codex structured workflows and an MCP server for building data pipelines with dlt - covering REST API ingestion, transformations, data quality, and exploration.

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 LLM agents to interact with the MOSTLY AI Platform for generating synthetic data, including training generators, managing data connectors, executing queries, and generating synthetic datasets with OAuth 2.1 authentication.

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.