ai_visibility_signal
$0.09 via x402: live feed of which brands/categories are being checked for AI-search visibility right now.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Optional signed x402 payment payload |
$0.09 via x402: live feed of which brands/categories are being checked for AI-search visibility right now.
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Optional signed x402 payment payload |
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 disclosure burden. It usefully discloses the cost ($0.09), payment mechanism (x402), and that the result is a live feed rather than a snapshot. It does not clarify whether the optional x_payment parameter is actually required, nor describe rate limits, update frequency, or response streaming behavior.
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 a single, front-loaded sentence that communicates the core value, pricing, and real-time nature without wasted words. It is easy to scan and gives an agent the essential facts immediately.
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 one optional parameter and no output schema, the description conveys the feed's subject and cost, but leaves ambiguity around the response format, whether payment is actually mandatory, and how to interpret 'live feed' operationally. The absence of an output schema raises the burden on the description to explain what the agent should expect back.
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 description coverage is 100%, so the optional x_payment parameter is already adequately documented in the schema. The description mentions x402 and the $0.09 cost but adds no new meaning about how to construct or use the payment payload beyond what the schema states.
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 clearly identifies a specific resource: a live feed of which brands/categories are being checked for AI-search visibility right now. The 'live feed' framing differentiates it from static checks like brand_ai_visibility_check or ai_visibility_index, though it does not explicitly name those siblings.
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?
The phrase 'right now' implies this is for real-time visibility monitoring, and the x402 payment detail hints at a paid feed. However, there is no explicit guidance on when to choose this over the many related visibility tools, nor any when-not-to-use instructions.
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.