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agent-ready-kit

by com.mudko

Create a binding-style proposal

create_proposal

Use this only when (a) quote confidence is medium or high, AND (b) user_consent_scope is 'ongoing_engagement' or 'one_time_quote'. Returns a proposal with terms, deliverables, and a checkout hand-off (ACP, AP2, or manual). Comparison-shopping callers are rejected with a 403-style error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
quote_idYes
selected_tierNo
checkout_protocolNo
user_consent_scopeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sowNo
reasonNoPresent when status=rejected.
statusYes
checkoutNo
next_actionNoPresent on manual protocol: the exact follow-up tool call that closes the deal autonomously (request_invoice with this proposal's quote_id — pay the invoice to trigger automatic delivery).
proposal_idNo
message_for_userNoPlain-language summary the calling agent can render to its end user. Never includes sales-pressure language.

TDQS

A4/5.0
Behavior4/5

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

Annotations lack behavioral info (readOnlyHint=false, etc.), but description adds: returns proposal with terms, deliverables, checkout hand-off types, and 403 error for comparison-shopping. Side effects and authorization are not fully disclosed, but it's sufficient.

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

Conciseness5/5

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

Two sentences with front-loaded conditions and key output. No wasted words; every sentence adds value.

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

Completeness3/5

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

High-level return behavior is described, but parameter details are sparse. Output schema likely covers return structure, yet the lack of parameter explanations leaves gaps for a tool with 4 params and 0% schema coverage.

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

Parameters2/5

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

Schema coverage is 0%, and description only partially explains 'user_consent_scope' via conditions. Other parameters ('quote_id', 'selected_tier', 'checkout_protocol') have no explanation, forcing reliance on names alone.

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 title 'Create a binding-style proposal' and description clearly state the tool creates a proposal. It distinguishes from sibling tools like 'refine_quote' and 'request_quote' by specifying conditions and output. Verb+resource is precise.

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

Usage Guidelines4/5

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

Explicit conditions for use are given: medium/high quote confidence and appropriate consent scope. It warns against comparison-shopping callers. However, no alternative tool is named for when conditions aren't met, leaving some ambiguity.

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

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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with descriptions that prevent confusion. Tools like scan_site and run_site_audit are differentiated by their focus on AI-readiness vs. site quality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, with clear action words like 'scan', 'create', 'get', 'verify'. Even longer names like 'summarize_scan_for_humans' maintain consistency.

Tool Count4/5

22 tools is slightly above the ideal range but justified by the comprehensive scope of the server, covering scanning, analysis, quoting, file delivery, and verification. Some tools like generate_files and get_customer_files could overlap but serve different contexts.

Completeness5/5

The tool surface covers the entire workflow from site scanning to deployment verification, with no obvious dead ends. All necessary operations for making a site agent-ready are present, including edge cases like x402 validation and credential verification.

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