Trellis vs AnyQuery MCP

Detailed side-by-side comparison to help you choose the right tool

Trellis

AI Knowledge Tools

An AI-powered document intelligence platform that transforms unstructured documents into structured, actionable data. Trellis leverages LLMs to extract, classify, and analyze information from complex documents at scale — supporting PDFs, scanned images, spreadsheets, and more — with a developer-friendly API and customizable output schemas for seamless integration into enterprise workflows.

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Starting Price

Custom

AnyQuery MCP

🔴Developer

AI Knowledge Tools

Revolutionary SQL-based tool that queries 40+ apps and services (GitHub, Notion, Apple Notes) with a single binary. Free open-source solution saving teams $360-1,800/year vs paid platforms, with AI agent integration via Model Context Protocol.

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Starting Price

Free

Feature Comparison

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FeatureTrellisAnyQuery MCP
CategoryAI Knowledge ToolsAI Knowledge Tools
Pricing Plans127 tiers4 tiers
Starting PriceFree
Key Features
  • Unstructured document parsing across PDFs, scanned images, spreadsheets, and Word documents
  • LLM-powered data extraction with high accuracy on printed and typed text
  • Custom schema mapping to define structured output fields per document type
  • SQL interface for 40+ apps and services
  • Model Context Protocol (MCP) server
  • Local-first privacy architecture

Trellis - Pros & Cons

Pros

  • Handles complex multi-format documents including PDFs, scans, and spreadsheets in a single pipeline without needing separate tools per format
  • LLM-powered extraction adapts to layout variations without requiring rigid templates for each new document format
  • Scalable batch processing architecture designed for enterprise-grade document volumes in the thousands per day
  • Developer-friendly REST API with customizable output schemas enables rapid integration into existing ETL and data workflows
  • Reduces manual data entry errors and turnaround times in document-heavy pre-service operations
  • Focused on operational document intelligence rather than general-purpose AI, providing purpose-built extraction workflows

Cons

  • Enterprise-focused pricing with custom quotes may be prohibitive for small teams, freelancers, or startups with low document volumes
  • Requires upfront schema configuration and pipeline setup before first extraction, adding time-to-value for new document types
  • Accuracy may degrade on handwritten documents or heavily degraded scans compared to clean typed or printed text
  • Limited publicly documented language support beyond English, which may restrict use for multinational organizations
  • No self-serve pricing page — prospective users must contact sales to evaluate cost, making it harder to budget in advance

AnyQuery MCP - Pros & Cons

Pros

  • Single static binary with zero runtime dependencies — install via Homebrew, Scoop, or direct download and it runs on macOS, Linux, and Windows without Docker or Node
  • Native MCP server mode exposes all 40+ connectors as structured tools to Claude, ChatGPT, Cursor, and other LLM clients with one command
  • Cross-source SQL joins let you combine GitHub issues with Linear tickets, Notion pages, and local CSVs in a single query — something Zapier and Power Automate cannot do
  • Speaks MySQL and PostgreSQL wire protocols, so existing BI tools (Metabase, Tableau, Grafana, DBeaver) connect without custom drivers
  • Fully local-first and open-source (AGPL) — no cloud tenant, no data egress, and no per-operation pricing, making it suitable for privacy-sensitive or regulated workloads
  • Supports read AND write operations (INSERT/UPDATE/DELETE) against sources like Notion, Airtable, and Todoist, not just read-only queries

Cons

  • Requires SQL fluency and terminal comfort — non-technical users who expect a Zapier-style visual builder will be lost
  • Connector quality is uneven: some integrations are maintained by the author, others are community plugins with varying update cadence and error handling
  • No managed scheduling, webhook triggers, or event-driven workflows — it answers queries on demand but won't replace an automation platform for reactive flows
  • Rate limits, pagination, and API quirks of upstream services (GitHub, Notion, etc.) still surface to the user; caching helps but doesn't fully hide them
  • Sole-maintainer project with a small contributor base, so long-term support, security patches, and enterprise-grade SLAs are not guaranteed

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