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Agentic Commerce Tools

by io.github.f-tiger

Agentic-commerce & MCP glossary lookup

define_term

Look up a plain-English definition of any agentic-commerce, MCP, AI-visibility or agent-payments term from the Agent Glossary (glossary.agiscorecard.com) — 25 terms including agentic commerce, ACP, UCP, MCP, MCP server, MCP tool, streamable HTTP, llms.txt, agents.md, GEO, AEO, citation share, AI Overviews, zero-click search, structured data, ChatGPT Shopping, x402, AP2, agentic payments, AI agent, RAG, function calling, A2A and prompt injection. Returns a one-paragraph citable definition plus the canonical glossary URL to link as the source. Matching is forgiving: case-insensitive and hyphen/space tolerant ('Streamable HTTP', 'streamable-http' and 'streamable_http' all resolve); an unknown term returns the full list of available terms. Answered inline from an embedded snapshot — no network round-trip, instant, free, unlimited within fair use. More agent tooling: free scanners on this server, pay-per-call APIs (x402 protocol, $0.005/call, USDC on Base, no account) at https://x402.agiscorecard.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYesThe term to define, by name or slug — e.g. 'agentic commerce', 'ACP', 'llms.txt', 'streamable-http', 'x402'. Case-insensitive; spaces, hyphens and underscores are interchangeable.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and succeeds well. It discloses the return shape (one paragraph plus canonical URL), unknown-term fallback behavior, input tolerance, offline snapshot execution with no network round-trip, and free/fair-use constraints. This is far richer behavioral disclosure than typical descriptions.

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 long, but the core purpose is front-loaded in the first sentence and subsequent sentences add genuinely useful details about coverage, matching, return value, and execution mode. The pricing and 'more agent tooling' sentence is somewhat tangential but not egregious.

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?

For a single-parameter tool with no output schema, this description is unusually complete. It covers the exact term list, matching rules, fallback behavior, response content, source URL, execution characteristics, and usage limits, making the tool independently understandable.

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% and the schema already describes case-insensitivity and separator tolerance. The description adds value by listing concrete example terms, naming the covered term categories, and explaining the unknown-term behavior that affects the single parameter's handling.

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 opens with a specific verb and resource: 'Look up a plain-English definition of any agentic-commerce, MCP, AI-visibility or agent-payments term from the Agent Glossary.' It clearly distinguishes the tool from sibling scanner/checker tools by framing it as a glossary lookup rather than a scanning or generation operation.

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?

The intended use case is clear: resolve glossary terms for agentic-commerce/MCP concepts. It does not explicitly name sibling alternatives or state when not to use them, but the lookup purpose is unambiguous and the sibling names make the division of labor obvious.

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.

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