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Agentic Commerce Tools

Agent-discovery well-known file audit

check_wellknown_discovery

Audit any website's agent-discovery surface in one call (free, via tools.agiscorecard.com). Checks the six files the agentic web uses to find and describe a business: /.well-known/ai-catalog.json (Agentic Resource Discovery manifest), /.well-known/mcp/server-card.json (MCP Server Card), /.well-known/agent-card.json (A2A Agent Card), /llms.txt, /agents.md, and robots.txt AI-crawler access (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot & co.). Returns a 0-100 discoverability score, per-file pass/warn/fail status with JSON validity checks, and a free generator link for every missing file. Use it to audit a merchant or SaaS site before agent integration, compare competitors' agent readiness, or produce a fix list. Complements agent_readiness_scan (page-level signals) with the well-known discovery layer. Free tier: please self-limit to a few calls per minute. High-volume / production agent use: pay-per-call APIs via the x402 payment protocol at https://x402.agiscorecard.com ($0.005 per call in USDC on Base — no account, no API key).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe website URL (or bare domain) to audit, e.g. https://example-store.com or example-store.com. https:// is assumed when the scheme is omitted; only the origin is checked.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It discloses the free tier, the six files checked, the return structure (score, per-file pass/warn/fail, generator links), and the rate limit plus paid option. No hidden side effects are implied; it is clearly a read-only audit.

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 description is a single dense paragraph but efficiently lists the six files, return values, and usage context. It is somewhat long but not padded; every sentence adds value. There is minor redundancy (e.g., 'and co.' could be replaced with full list) but structure flows logically.

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?

Given no output schema and a single parameter, the description thoroughly explains what the tool returns (score, per-file status, generator links), when to use it, and the payment/rate-limit context. It is complete enough for an agent to decide and invoke without additional clues.

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

Parameters3/5

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

Schema description coverage is 100% for the single 'url' parameter, so the schema already explains format and defaults. The description adds no extra parameter-specific meaning beyond the schema, meeting the baseline for full schema coverage.

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 description clearly states the tool audits a website's agent-discovery surface, enumerates the six specific files it checks, and contrasts with sibling agent_readiness_scan (page-level signals) making it distinct. The verb 'Audit' and resource 'agent-discovery surface' are specific and unambiguous.

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?

It explicitly states when to use: 'audit a merchant or SaaS site before agent integration, compare competitors' agent readiness, or produce a fix list.' It also differentiates from agent_readiness_scan and provides rate-limit guidance ('please self-limit to a few calls per minute') and alternative paid usage.

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

A4.2/5.0
Disambiguation4/5

All tools have distinct purposes: scanning, checking MCP servers, well-known discovery, term lookup, generation, and index retrieval. However, agent_readiness_scan and check_wellknown_discovery overlap in checking AI-readiness signals, though one focuses on page-level and the other on discovery files, which may cause some confusion.

Naming Consistency3/5

Most tools follow a verb_noun pattern (agent_readiness_scan, check_mcp_server, check_wellknown_discovery, generate_llms_txt, get_public_mcp_index, get_visibility_index), but define_term is a different style (verb + object without underscore). Minor inconsistency in the use of underscores vs. no delimiter for 'wellknown'.

Tool Count4/5

Seven tools is a reasonable count for a server covering agent-readiness auditing and related utilities. Each tool has a clear role, though some could be seen as variations of the same theme, but the count is within the ideal range.

Completeness4/5

The server covers the main workflows: scanning individual sites for readiness, checking MCP servers, and accessing aggregate indexes, plus generation and reference. A gap might be an update or deeper analysis tool (e.g., a tool to get recommendations for multiple sites at once), but overall it feels complete for its stated purpose.