"Understanding Integrated Knowledge Graphs" matching MCP servers:

MCP server for querying procurement and inventory documents (invoices, purchase orders, etc.) via structured lookups, precomputed comparisons, and full-text search.

Provides MCP tools for AI assistants to retrieve compact daily futures data packets, submit manual market data, run whitelisted crawlers, search research summaries, and save/read historical reports on demand for caustic soda, PVC, and coking coal futures.

A read-only MCP server for querying SQL Server Analysis Services (multidimensional cubes via MDX and tabular models via DAX) using Windows Integrated Security.

Provides tools for managing qualitative research knowledge graphs, enabling structured representation of research projects, participants, interviews, observations, codes, themes, and findings.

MCP server for the BAIC Data Layer. Provides tools to manage sources, knowledge bases, knowledge clusters, run data agents, and create/manage pipelines.

Enables loading and statistical analysis of .xlsx and .csv files with visualization capabilities using matplotlib and plotly to generate various graphs and charts.

A Model Context Protocol server enabling AI assistants to interact with TriliumNext notes, providing tools for creating, searching, reading, and updating notes in your knowledge base.

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.

Provides tools for named entity extraction, Wikidata disambiguation, sameAs schema linking, and knowledge graph triple construction, enabling structured semantic data processing.

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 reading, summarizing, searching, validating, and surgically editing Pentaho Kettle .kjb/.ktr files without requiring a PDI installation or executing them, using an embedded knowledge base.

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 to query permission-filtered world views, inspect evidence-backed signals, and trace timelines across integrated enterprise systems, handing deep investigation off to vertical MCP servers via verified capabilities.

Transforms CSV data into beautiful visualizations including line charts, bar graphs, pie charts, and world maps. Returns base64-encoded PNG images optimized for AI chat interfaces and assistants.

Enables autonomous trading agents to query normalized cross-asset event intelligence and per-asset impacts from primary sources, including events since a time, exposure graphs, regime snapshots, and explanations. It supports MCP over streamable HTTP or stdio, with paid per-call access in USDC via x402 or API keys.

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

Provides deterministic tools for understanding, transforming, and verifying structured data via MCP, enabling rule inference from examples and verification of transformed records.

Provides full lineage graph analysis for Power BI .pbix files, enabling source-to-visual-field traversal. Exposes REST and MCP endpoints for building, searching, and exporting lineage graphs.

A conversational copilot that guides users through the Knowledge Discovery in Databases (KDD) process to perform automated data profiling, analysis, and modeling via natural language. It enables the creation of interactive dashboards and manages the end-to-end analytical workflow within a secure sandbox environment.

Exposes an agentic AutoML pipeline as a callable tool so any MCP client can upload a CSV and natural language goal and receive a full ML analysis report with SHAP visualizations. Enables autonomous problem understanding, data cleaning, feature engineering, model training, and explainability through a single remote tool call.