"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.

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.

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.