$0.09 via x402: download counts for any npm package over the last day/week/month. Live from the npm registry stats API.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | last-day|last-week|last-month | |
| package | Yes | ||
| 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?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It does disclose the cost ($0.09 via x402) and the live data source, which is useful. However, it does not explicitly state that the operation is read-only, nor does it mention potential rate limits, errors for invalid packages, or return format. This is a moderate level of transparency.
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, well-structured sentence that front-loads the price and core functionality. Every word adds value: price, tool name, resource, time periods, and data source. No wasted or redundant phrasing.
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 simple tool, the description covers purpose, period options, data source, and cost. However, it does not describe the output format (e.g., a number or object) and does not clarify how to provide the x_payment value. Given the absence of an output schema and low parameter coverage, these omissions leave some ambiguity for the agent.
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 low (33%), so the description must compensate. It clarifies that 'package' is an npm package name and that 'period' corresponds to day/week/month, matching the schema hint. However, 'x_payment' is not explained beyond the price hint in the description, leaving its usage ambiguous.
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 the tool's purpose: providing download counts for any npm package over the last day, week, or month. It uses a specific verb ('download counts') and resource ('npm package'), and stands apart from all sibling tools which focus on blockchain, crypto, or other domains.
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 clearly implies when to use this tool: whenever npm download statistics are needed. There are no sibling tools offering similar functionality, so no explicit alternatives are needed. However, it doesn't explicitly exclude other periods or mention any prerequisites beyond the payment via x402.
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.