MCP servers for your data
Tool-based interfaces that expose business or institutional data in a way AI agents can query and use.
Early infrastructure for AI-ready data
Make your data usable by AI agents.
AndesProtocol builds agent-ready interfaces for Latin American data, APIs, documents, and institutional systems.
We help organizations expose their information through structured, multilingual, auditable, and agent-optimized interfaces.
What we build
Agent-ready data interfaces
Structured outputs for MCP and A2A-style workflows, with source traceability and multilingual access built in.
Problem
Your data exists. Agents still cannot use it reliably.
Many companies and institutions already have valuable data, but it is scattered across portals, APIs, PDFs, spreadsheets, internal systems, or public records. Human users can navigate that complexity. AI agents need something different: predictable schemas, clear provenance, multilingual responses, and machine-actionable interfaces.
Solution
What AndesProtocol builds
Agent-ready APIs
Predictable endpoints and response shapes designed for retrieval, interpretation, and downstream automation.
Multilingual retrieval
Access patterns that preserve local language context while supporting broader agent use across English and Spanish.
Source-preserving outputs
Responses that keep original identifiers, references, and evidence visible instead of hiding provenance.
Structured responses
Normalized fields and stable formats that reduce ambiguity for agents and for the teams that operate them.
Audit and traceability
Data flows that make it easier to inspect what came from the source, what was normalized, and what was derived.
Initial MVP
ChileCompra MCP
Our first prototype focuses on Chilean public procurement data. It explores how an AI agent can search, interpret, and retrieve procurement opportunities through structured MCP tools.
Next Vertical
CMF MCP
The next research vertical will explore financial disclosures, balances, regulated entities, and financial health summaries using public data from Chile's financial regulator.
Commercial Services
From public data prototypes to business integrations
The same architecture can be applied to private companies, public institutions, startups, consulting firms, and regulated industries that need to expose their data to AI agents safely and clearly.
Turn internal APIs into agent-ready tools
Convert documents and datasets into structured interfaces
Build MCP servers for business workflows
Add multilingual access for global agents
Preserve original sources for audit and compliance
Design Principles
Interfaces for agents should still be clear to people.
Human-readable when needed.
Machine-optimized when possible.
Auditable always.
Multilingual from the start.
Local context preserved.
Next Step
Prepare your data for the agent era.
AndesProtocol is starting with Chilean public data, but the goal is broader: helping Latin American organizations become usable by AI agents.