$0.09 via x402: proprietary AI-visibility scores for 25 major brands across 5 industries, with competitors AI names instead.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category filter | |
| 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does mention the $0.09 x402 payment requirement, which is useful, but it does not disclose whether the operation is read-only, whether payment is strictly required given x_payment is optional, what happens on missing payment, or what response shape to expect. This is insufficient for a paid data-access tool with no annotation safety profile.
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 short and mostly direct, but it is a fragment rather than a complete sentence. It front-loads the price before the core purpose, and the ambiguous 'instead' clause undermines clarity while still consuming space. It is concise but not optimally structured for agent comprehension.
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, no annotations, and no explanation of the return format, so the description must be self-sufficient. It does not explain what 'with competitors AI names instead' means, whether the 25 brands are fixed or selectable, or how the optional category and payment parameters affect results. For an agent to invoke this tool correctly, important context is missing.
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 schema already describes both parameters as optional filters. The description's mention of '$0.09 via x402' adds some context around x_payment, but it does not clarify category values, payment payload format, or how the parameters interact. Since the schema already covers parameter names and roles, the description adds only marginal semantic value.
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 states the tool provides proprietary AI-visibility scores for 25 major brands across 5 industries, which is a concrete resource and scope. However, it lacks an explicit verb and the trailing clause 'with competitors AI names instead' is ambiguous, making the exact purpose unclear. It does not clearly differentiate itself from similar sibling tools like brand_ai_visibility_check or ai_category_ranking.
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 description gives no guidance on when to use this tool versus the many related siblings such as ai_visibility_signal, brand_ai_visibility_check, or ai_category_ranking. There is no mention of exclusions, prerequisites, or alternative tools, so the agent must infer the intended use case.
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.