AgentDiet Automated /llms.txt & Agent Documentation Context Compiler

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds real context beyond that: it never charges a fee or consumes a free trial call, and the default wallet is derived from the caller's client IP. It does not discuss rate limits or caching, but the cost/free-trial guarantee is meaningful behavioral disclosure.

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

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

Front-loaded with the ledger contents, then structured WHEN TO USE / WHEN NOT TO USE / PARAMETER INTERACTIONS / Example. The labeled sections make it scannable, though the repetition of the read-only guarantee and the worked example add a little bulk.

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

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

An output schema exists, so return-value detail is optional, yet the description still names the key fields. With a single optional parameter, no nested objects, and annotations covering safety, an agent has everything needed to invoke it correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description goes slightly further by naming the agd_pass_ session-key form and framing the two modes as an interaction (omit for IP-derived trial account vs. pass explicit agent_id/session key for a funded wallet), which aids selection even though the schema states the omit behavior.

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

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

States a specific verb (inspect) and resource (agent's account ledger) and enumerates exactly what is returned: trial calls out of 5, prepaid USD balance, cumulative spend, total API calls, tokens saved. This clearly separates it from the sibling web-content tools (distill_webpage, extract_structured_data, recon_webpage_skeleton), which operate on webpages rather than wallet state.

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

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

Explicit WHEN TO USE (before paid tool batches, after a Stripe or USDC top-up) and WHEN NOT TO USE (not for processing web content, executing marketplace tools, or submitting payments). The exclusion clause routes the agent back to the other server tools for execution, so the alternative is named.

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