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AI Agent, MCP Server & Prompt-Injection Readiness Suite ($0.05)

suite_ai_agent_and_mcp_readiness
Idempotent

[Paid x402 / X-Bazaar-Key: 0.05 USDC] Composite 6-check autonomous agent readiness audit chaining A2A agent-card.json, MCP manifest, MCP token budget, llms.txt, OpenAPI tool lint, and prompt-injection fencing. Free sample report: https://bot-bazaar.fly.dev/v1/suite-samples/ai-agent-and-mcp-readiness

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "suite_ai_agent_and_mcp_readiness"
  }
}

Input Schema

NameRequiredDescriptionDefault
agent_nameNoTarget AI agent or MCP server name

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover safety (destructiveHint=false, idempotentHint=true, openWorldHint=true), and the description adds material context beyond them: this is a paid x402 endpoint at 0.05 USDC via X-Bazaar-Key, and a free sample report is available for preflight. It does not explain what the audit actually consumes as its target (no URL/spec parameter exists beyond an optional agent_name), which is the one behavioral question left open.

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?

Two dense sentences, front-loaded with the paid-scheme marker and the composite scope, with the sample URL last. The mid-sentence list of six checks is long but each item earns its place by defining the bundle's coverage.

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?

There is no output schema, so the description must stand in for return semantics; the free sample URL partially covers that but does not state the report shape or verdict format. It is also silent on how the target is identified given that agent_name is optional (0 required params), which is a meaningful gap for a paid call costing 0.05 USDC.

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?

With a single parameter at 100% schema description coverage, the schema carries the load and the description adds no conflicting or missing parameter meaning. Baseline 4 applies since nothing is left undocumented.

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

Purpose4/5

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

The description names a specific verb (audit) and resource (AI agent / MCP server readiness) and enumerates the six chained checks, so an agent can distinguish this composite from individual siblings like mcp_manifest_lint, llms_txt_lint, and openapi_tool_lint. It does not explicitly say 'use this instead of the six single lints', but the enumerated coverage makes the differentiation inferable.

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

Usage Guidelines3/5

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

Usage is implied by 'readiness audit' but there is no explicit when-to-use, when-not-to-use, or routing statement versus the sibling single-purpose lints (mcp_manifest_lint, agent_card_lint, llms_txt_lint) or the other suite_* bundles. The free sample-link points at output, not at selection criteria.

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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