$0.09 via x402: Company/Domain Enrichment Connector — RDAP domain age + registrar, DNS email provider, and a Wikipedia company profile, fused in one call. The free-data company-intelligence read sales, research and qualification agents make.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Company domain, e.g. stripe.com | |
| x_payment | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It communicates that this is a read operation, discloses the $0.09 x402 payment cost, lists the data sources, and notes the one-call fusion behavior. It does not describe error handling, response format, or what happens on invalid domains, but the disclosed payment and read-only nature are substantial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loads the payment and primary enrichment purpose before the data-source breakdown. The final sentence is slightly awkward ('read sales, research and qualification agents make') but still earns its place by stating the intended audience and use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description at least lists the expected data types (RDAP age/registrar, DNS email provider, Wikipedia profile). However, it does not clarify the x_payment parameter format or how the x402 payment is actually triggered, and it omits return structure details. It is enough to select the tool but not fully enough to invoke it with complete confidence.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%: only 'domain' is described, and x_payment is undocumented. The description adds meaning for domain by framing it as company intelligence, and the '$0.09 via x402' line loosely hints at x_payment's role, but it never explicitly explains how or whether x_payment must be supplied. This is partial compensation, not full.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource (company/domain) and enumerates exactly what the tool does: RDAP domain age and registrar, DNS email provider, and Wikipedia company profile fused into one call. This clearly distinguishes it from generic enrichment tools like b2b_lead_enrichment or wikipedia_lookup by specifying the unique data combination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a clear usage context: 'the free-data company-intelligence read sales, research and qualification agents make.' This tells an agent when to reach for this tool. It does not explicitly name alternatives or say when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.