"Understanding Code Relationships or Dependencies" matching MCP servers:

Data compression MCP server with auto-algorithm selection (gzip, brotli, deflate). 7 tools for compress, decompress, analyze, store, retrieve, list, and stats. Achieves 60x compression on docs, 30x on SQL. Lossless round-trip verified. Zero dependencies.

Enables local Power BI project (.pbip) automation, including model (TMDL) and report (PBIR) layer manipulation, without cloud dependencies. Provides 56 tools for building, editing, and validating reports and measures through natural language prompts.

A Model Context Protocol server that allows interaction with the RubyGems.org API to fetch metadata about Ruby packages, search gems, and explore dependencies and ownership information.

A Python MCP server for hypothesis-driven data analysis, managing analysis designs, data catalogs, and review workflows through Claude Code or any MCP-compatible client.

Enables read-only access to FileMaker databases through the Data API, allowing users to retrieve records, analyze metadata, search across layouts, and infer relationships while maintaining data security.

Enables creating and inspecting Seed tables and relationships through the Seed backend HTTP API.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.

A Python MCP server that lets you manage Microsoft Fabric through natural language in Claude Code or Claude Desktop, with 77+ tools covering workspaces, lakehouses, warehouses, SQL, DAX, semantic models, notebooks, pipelines, OneLake, and Microsoft Graph.

Enables authenticated users to manage paper-trading accounts, place and cancel orders with simulated fills, view positions and P&L, search instruments, fetch bars, and run quant-strategy lifecycles over stdio or streamable HTTP.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

Enables AI assistants to read a verified household financial record across linked accounts, including holdings, transactions, fees, performance, cash flow, advisors, diagnostics, and score findings. It can also refresh connections and manage manually tracked assets or liabilities inside the service, but cannot move money or place trades at financial institutions.

Enables an AI assistant to build Power BI projects end to end: data modeling (tables, DAX measures, relationships, calculation groups, roles, field parameters) against an open Power BI Desktop via TOM/XMLA, and reports (pages, visuals, formatting, bookmarks, themes) as PBIR files in a .pbip project. Every visual is cross-checked against the real model before being written, so generated dashboards don't break on open.

Enables querying open Power BI Desktop models locally, including discovering instances, listing tables and measures, and running DAX queries without cloud login or publishing.

A read-only Model Context Protocol server for Microsoft Fabric, enabling natural language queries to workspaces, lakehouses, tables, jobs, and dependencies.

Enables AI agents to query Mixpanel event data, funnel conversion rates, and retention cohorts via MCP tools or natural language.

Enables language models to run data-quality checks and profiling on local files, using dbt-style assertions like not_null, unique, relationships, and accepted_values.

Enables Claude Code to generate Kafka producer and consumer code using predefined event schemas, with tools to list events, get schemas, and validate code.

Enables interaction with the Bigeye Data Observability platform to query data quality issues, analyze data lineage and dependencies, track AI agent data access patterns, and perform root cause analysis for data issues.

Enables agents to explore a project's captured events and properties, then run trend, funnel and retention analyses and create or list saved insights. Tools are model-oriented, returning trimmed, budgeted results and actionable errors, with the project resolved from the API key or bearer token rather than passed as an argument.

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.