LLMs.txt for Upside

Upside publishes machine-readable documentation files that coding assistants and custom agents can consume without parsing the rendered website. Use them to discover the API surface, retrieve implementation details, generate integration code, and ground agent responses in the current documentation.

The machine-readable files describe the API; they do not grant access or bypass signing. Read requests still use POST /info, while state-changing requests require a valid signed envelope sent to POST /exchange.

Available files

llms.txt

A compact index of documentation pages with titles, descriptions, and links. Start here when an agent needs to discover the right source before loading more context.

llms-full.txt

The full published documentation rendered as one Markdown document. Use it for broad code-generation tasks, offline indexing, or retrieval pipelines.

https://upside.mintlify.app/llms.txt
https://upside.mintlify.app/llms-full.txt

Which file should you use?

Task	Recommended source	Why
Find the page for an endpoint or concept	`llms.txt`	Fast, compact discovery with minimal context usage
Answer a focused integration question	`llms.txt`, then the linked page	Keeps the context narrow and easier to verify
Generate a client spanning several API areas	`llms-full.txt`	Provides read, write, signing, and WebSocket context together
Build a documentation search or RAG index	`llms-full.txt`	One normalized Markdown corpus is easy to chunk and embed
Keep a long-running agent context fresh	`llms.txt` plus selected pages	Avoids repeatedly loading the full corpus

Prefer a two-stage retrieval flow: use llms.txt to select the relevant pages, then load only those pages. Reserve llms-full.txt for tasks that genuinely require cross-document context.

Recommended retrieval workflow

Fetch the index

Load llms.txt and identify the pages related to the user’s task, such as authentication, order placement, account state, or WebSocket subscriptions.

Retrieve authoritative pages

Fetch the selected documentation pages and keep their URLs or paths alongside the extracted text so the agent can cite its source.

Generate a structured proposal

Ask the model for typed data or code rather than an unstructured instruction. For trading actions, require a JSON proposal that can be validated before signing.

Validate outside the model

Apply strict schemas, precision rules, risk limits, allowed-action lists, and nonce management in deterministic application code.

Refresh when the docs change

Re-fetch the index at application startup or on a controlled schedule, then update only the changed pages in your local cache or retrieval index.

Use with coding assistants

Claude Code or similar CLI agents

Fetch the full document when the task spans multiple sections:

/fetch https://upside.mintlify.app/llms-full.txt

For a focused task, fetch the index first and then the specific page it points to.

Cursor, Windsurf, and IDE assistants

Add one of the URLs as a documentation source in the project’s context settings. Use llms.txt for everyday lookup and llms-full.txt for repository-wide client generation or refactoring. A useful project rule is:

Before implementing or changing Upside DEX integration code, consult the
machine-readable documentation. Do not infer request fields or response shapes
from naming alone. Link each non-obvious implementation choice to its source page.

Custom agents

Fetch the index with curl:

curl -fsSL https://upside.mintlify.app/llms.txt

Download the full corpus:

curl -fsSL https://upside.mintlify.app/llms-full.txt \
  -o upside-llms-full.txt

A minimal Python loader:

from __future__ import annotations

import requests

DOCS_URL = "https://upside.mintlify.app/llms.txt"


def load_docs(url: str = DOCS_URL) -> str:
    response = requests.get(url, timeout=20)
    response.raise_for_status()
    return response.text


documentation_index = load_docs()
print(documentation_index[:500])

Prompt recipes

Endpoint discovery

Using the Upside documentation index, identify the smallest set of pages
needed to implement this task. Return the page URLs first, then summarize the
required endpoint, request fields, response fields, signing requirements, and
failure cases. Do not write code until the sources are identified.

Code generation

Generate a production-oriented implementation from the cited Upside pages.
Use exact field names and preserve documented numeric types. Separate payload
construction, validation, signing, transport, and response reconciliation.
Never include a real private key or secret in examples.

Payload review

Compare this proposed request against the Upside documentation. Report:
unknown or missing fields,
type or precision mismatches,
signature and nonce requirements,
unsafe assumptions,
the corrected payload.
Cite the documentation page supporting each correction.

Building a retrieval index

When indexing llms-full.txt for retrieval-augmented generation:

Split primarily at page titles and section headings rather than arbitrary character counts.
Store each chunk with its page URL, heading path, and document version or fetch time.
Keep code blocks with the paragraph that explains them.
Give authentication, nonce, error handling, and action-specific pages higher retrieval priority for write operations.
Return source links with every generated answer so developers can verify the recommendation.
Treat retrieved documentation as context, not executable authority; validate all generated writes independently.

High-value documentation paths

Authentication

Canonical JSON, signed-message construction, keccak256, and ECDSA signature formatting.

Nonce

Uniqueness and replay-protection requirements for every signing address.

Read API

Market, account, agent, and order queries through POST /info.

Write API

State-changing actions and the shared POST /exchange envelope.

WebSocket

Real-time market and private account streams for synchronized agent state.

Agent wallets

Authorization, expiry, monitoring, rotation, and revocation for automated signers.

Do not place private keys, seed phrases, signing-service credentials, or unrestricted production permissions in an LLM context. The model should produce an intent or unsigned payload; a separate trusted service should validate and sign it.

Get Started

Trading Agents

Available files

llms.txt

llms-full.txt

Which file should you use?

Recommended retrieval workflow

Use with coding assistants

Claude Code or similar CLI agents

Cursor, Windsurf, and IDE assistants

Custom agents

Prompt recipes

Endpoint discovery

Code generation

Payload review

Building a retrieval index

High-value documentation paths

Authentication

Nonce

Read API

Write API

WebSocket

Agent wallets

​Available files

llms.txt

llms-full.txt

​Which file should you use?

​Recommended retrieval workflow

​Use with coding assistants

​Claude Code or similar CLI agents

​Cursor, Windsurf, and IDE assistants

​Custom agents

​Prompt recipes

​Endpoint discovery

​Code generation

​Payload review

​Building a retrieval index

​High-value documentation paths

Authentication

Nonce

Read API

Write API

WebSocket

Agent wallets

Available files

Which file should you use?

Recommended retrieval workflow

Use with coding assistants

Claude Code or similar CLI agents

Cursor, Windsurf, and IDE assistants

Custom agents

Prompt recipes

Endpoint discovery

Code generation

Payload review

Building a retrieval index

High-value documentation paths