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Riley Craig x402 Agent Store

by io.github.rccola990-cloud

sales_qualify_lead

PREMIUM ($0.09 via x402): qualify a sales prospect by its AI-visibility gap — live audit returns HOT/WARM/COLD lead tier, score 0-100, who AI recommends instead, a factual ready-to-personalize outreach opener, and a 30-day follow-up plan. For SDR/sales agents and agencies selling GEO/SEO/marketing services. Sends nothing itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesProspect brand or business name
marketNous|uk|de|jp|kr|fr|es|br|in, default us
categoryYesWhat the prospect's buyers search for, e.g. 'CRM software'
x_paymentNoOptional signed x402 payment payload (X-PAYMENT header value)
seller_serviceNoWhat YOU sell to this prospect (shapes the angle)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added
  2. Removed
  3. Added
  4. Removed
  5. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description discloses key behaviors: it is a premium tool costing $0.09 via x402, performs a 'live audit', and 'Sends nothing itself' – indicating no external side effects. It also lists the specific output components, but doesn't disclose data sources or potential failure modes, so not a 5.

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, highly scannable, with the premium warning and cost front-loaded. Every clause adds value: purpose, outputs, audience, and side-effect disclosure. No wasted words.

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

Completeness4/5

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

Given no output schema, the description explains the return values in text (lead tier, score, recommendation, opener, follow-up plan). It covers cost and target user, and clarifies no external sends. It doesn't address input limits or failure behavior, but for a lead-qualification tool this is adequate.

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?

Input schema has 100% description coverage, so the baseline is 3. The description adds context about the sales use case and mentions 'seller_service' indirectly ('shapes the angle'), but doesn't elaborate on parameter syntax or formats beyond the schema.

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 uses a specific verb 'qualify' and resource 'sales prospect', then details concrete outputs (HOT/WARM/COLD tier, score 0-100, AI recommendation, outreach opener, follow-up plan). It clearly distinguishes itself from sibling tools like b2b_lead_enrichment by focusing on AI-visibility gap.

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?

It states the target audience ('For SDR/sales agents and agencies selling GEO/SEO/marketing services') and the context of pre-outreach qualification. It doesn't explicitly name alternatives or exclusion criteria, but the unique 'AI-visibility gap' framing makes the use case clear.

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

C2.7/5.0
Disambiguation2/5

Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.

Naming Consistency2/5

Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.

Tool Count1/5

At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.

Completeness3/5

The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.

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