PREMIUM ($0.09 via x402): live brand AI-visibility audit — real buyer questions through an LLM, score 0-100, mention rate, competitors AI names instead, full evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | Brand or business name | |
| market | No | us|uk|de|jp|kr|fr|es|br|in, default us | |
| category | Yes | Buyer search category, e.g. 'CRM software' | |
| x_payment | No | Optional signed x402 payment payload (X-PAYMENT header value) |
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, the description carries the burden and does disclose cost, methodology (real buyer questions through an LLM), and key outputs (score, mention rate, competitor names, evidence). However, it does not specify side effects, permission requirements, rate limits, or what happens when the x402 payment is absent, and 'full evidence' remains vague.
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 entire description is one dense sentence, but it packs in price, payment method, methodology, and outputs without obvious filler. It is front-loaded with the premium cost and purpose, making it compact and reasonably well structured, though the telegraphic style slightly hurts readability.
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?
There is no output schema or annotations, so the description must explain expected outcomes; it lists the main outputs but omits payment/payload behavior, market default implications, and any error or edge-case handling. It is adequate for a simple audit tool but incomplete for a paid API without a defined response contract.
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?
The input schema already provides full descriptions for all four parameters, so baseline 3 is appropriate. The description adds little beyond the schema—it mentions score and audit but does not detail how brand, category, market, or x_payment affect the call.
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 states a live brand AI-visibility audit with specific outputs: score 0-100, mention rate, competitor AI mentions, and evidence. The verb 'audit' plus the resource 'brand' gives a specific purpose, and the LLM-based buyer-question methodology distinguishes it from sibling tools like ai_visibility_index or ai_visibility_signal.
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?
There is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites beyond 'PREMIUM' and x402 payment. The description implies it is for brand AI-visibility measurement, but it does not name sibling tools or provide decision criteria.
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.