> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hasmcp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# What are the two primary methods HasMCP provides for context optimization?

> Managing how API payloads are structurally pruned before executing LLM logic.

# Context Optimization Methods

HasMCP exclusively uses two interceptor engines designed to slice, prune, and transform heavy `JSON` payloads received from external provider APIs before they impact the final LLM Context Window:

### 1. JMESPath Pruning

**JMESPath** is a declarative query language built for slicing JSON natively. It serves as a rapid structural filter mechanism.

* **Ideal For:** Extracting subsets of matrices (`data.results[].{id: object_id, value: metadata}`), filtering specific nodes, isolating exact strings nested in heavy arrays.
* **Benefit:** Very fast execution. Safe declarative syntax preventing infinite loops.

### 2. Goja (JavaScript) Interceptors

**Goja** is a pure functional JavaScript execution engine built to parse deeply complex or logical data transformations securely at the proxy edge.

* **Ideal For:** Math operations (summing array values), dynamic redaction rules (detecting regex string boundaries), parsing complex non-standard encodings (`base64`).
* **Benefit:** Absolute programmatic freedom to reconstruct the exact `JSON` object your specific Agent expects organically.

> \[!IMPORTANT]
> **Execution Timeouts**: Both GoJA and JMESPath interceptors enforce a strict **100ms execution timeout** on HasMCP Cloud versions to ensure real-time proxy speed and prevent infinite loops. Enterprise on-prem versions allow administrators to define their own custom timeout values depending on their infrastructure capabilities.