AgentDiet Automated /llms.txt & Agent Documentation Context Compiler

generate_site_llms_txt

Read-onlyIdempotent

Crawls any documentation or SaaS site and compiles a standards-compliant /llms.txt manifest and JSON-LD schema bundle. WHEN TO USE: Call generate_site_llms_txt when your task requires deterministic llms-generator analysis for url; use get_agent_wallet_status to check your remaining free trial quota or prepaid balance. WHEN NOT TO USE: Do not use for unrelated domains or binary media. PARAMETER INTERACTIONS: Pass the target url as a non-empty string; omit agent_id and tx_hash during your 5 free trial calls, or supply agent_id (agd_pass_...) / tx_hash for paid execution. Read-only and idempotent; results are cached for 1 hour (<5ms cache hit). $0.01 per call. Pricing: the first 5 calls per agent_id are free and failed calls are never charged. After 5 calls, the tool returns a payment-required quote (not a fatal error) with a $2 Stripe top-up link and Base/Solana USDC addresses; complete payment, then retry with your agd_pass_ key in agent_id or pass tx_hash. Example: generate_site_llms_txt(url='https://docs.stripe.com/api')

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "https://docs.stripe.com/api"
    },
    "name": "generate_site_llms_txt"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	Website URL to crawl and generate a ready-to-consume /llms.txt manifest for.
`tx_hash`	No	Optional on-chain Base Mainnet (0x...) or Solana SPL USDC transaction hash. Omit unless settling an individual call directly on-chain instead of using an agd_pass_ key in agent_id.
`agent_id`	No	Optional agent identifier or prepaid agd_pass_ session key. Omit during your 5 free trial calls to use an ID automatically derived from your client IP, or pass your agd_pass_ key after topping up.

Output Schema

Name	Required	Description
`data`	No	Structured tool output payload.
`status`	Yes	Execution status ('ok' or 'payment_required').
`roi_proof`	No	Token compression and cost-savings verification metrics.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, but the description adds substantial context beyond them: 1-hour result caching with <5ms cache hits, $0.01 per-call pricing, 5 free calls per agent_id, and the non-fatal payment-required quote with retry mechanics. This is exactly the behavioral detail annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded and the WHEN TO USE / NOT / PARAMETER INTERACTIONS structure is scannable. It is somewhat longer than needed, with pricing and payment details restated across two sentences, but every block is purposeful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return-value description is not required, and the description still covers invocation prerequisites, billing gates, and failure recovery. An agent has everything needed to call this successfully on the first attempt.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds interaction semantics the schema only partially states: omit agent_id/tx_hash during free trials and supply agent_id (agd_pass_...) or tx_hash for paid execution. It earns above baseline by clarifying how the three parameters combine rather than describing them individually.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence gives a specific verb (Crawls) and concrete outputs (/llms.txt manifest and JSON-LD schema bundle) for a named input domain (documentation or SaaS sites). An agent can distinguish this from get_agent_wallet_status without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit WHEN TO USE / WHEN NOT TO USE blocks name the trigger condition and route quota checks to the sibling tool get_agent_wallet_status. Exclusions are stated (unrelated domains, binary media), leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All AgentDiet Automated /llms.txt & Agent Documentation Context Compiler tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.