"Carto" matching MCP servers:

Enables AI agents to forecast asset price paths using Monte Carlo simulation with EGARCH volatility and skewed-t shocks, providing risk metrics and percentiles.

Cartograph is an MCP server that provides agent-native code intelligence by building a queryable code graph from a repository using tree-sitter and SQLite. It offers tools for symbol lookup, call graph traversal, blast radius analysis, and architecture overview to help coding agents answer structural questions about code.

Computes soil resource concern ratings for an area of interest using USDA Soil Data Access and exposes them as MCP tools, resources, and prompts for AI-assisted conservation planning.

Exposes a focused widget workflow for agents, enabling search, inspect, install, create, validate, checkin, and configuration of Cartograph widgets without mirroring the full CLI.

Provides native MCP tools for quantitative-finance and risk-modeling workflows, enabling computation of option Greeks, simulation of geometric Brownian motion asset-price paths, historical/parametric VaR and CVaR estimates, bond duration/convexity metrics, and Nelson-Siegel yield-curve interpolation using only Python's standard library.

Enables agents to translate natural-language shopping intent into aggregated multi-merchant item discovery, consolidated cross-merchant carts, and structured transaction checkout payloads. Runs as a zero-dependency, MCP-compatible engine that can also be imported directly as a Python module.

Enables monitoring abandoned checkout sessions, diagnosing friction causes, crafting margin-preserving incentive vouchers, and generating multi-channel win-back messages through MCP.

Enables AI assistants to search Australian retailers and fill the user's own online shopping cart through tools for product search, adding items, setting quantities, and checking connection and usage status. Supports Woolworths today, with Coles, Amazon Australia, Kmart and BIG W planned.

Enables agents to translate consumer shopping intent into aggregated item discovery, cross-merchant cart consolidation, and structured transaction checkout payloads across multiple merchants. It runs as a zero-dependency Python MCP server or importable module compatible with Claude Desktop, Cursor, and other work agent frameworks.

Enables mathematical proof exploration and verification over MCP, where each result includes a certificate that can be checked independently without trusting the solver.

Enables users to diagnose cart abandonment friction, generate margin-preserving incentive vouchers, and synthesize channel-native win-back messages across commerce platforms. It supports recovery orchestration for abandoned checkout sessions in systems such as Meta Muse, Shopify, and WhatsApp Commerce.

Maps a local Python repository into an AST-backed symbol graph and performs verified multi-file refactors, enabling scope-aware renaming, moving, and deletion of symbols with safety checks.

Enables quantitative finance and risk analysis through MCP, including geometric Brownian motion Monte Carlo simulations, Black-Scholes Greeks, VaR/CVaR, bond duration/convexity, and Nelson-Siegel yield curve interpolation.

Provides a suite of tools for running Cargo commands and interacting with Rust projects through AI agents. It enables automated builds, testing, linting, and documentation generation directly from any MCP-compatible client.

Connects Claude to Cartola FC, enabling queries about matches, player scores, and market information through natural language.

Enables AI agents to browse product catalogs and make purchases through a policy engine that enforces spending limits, requires human approval for certain amounts, and logs all actions to an audit trail.