$0.09 via x402: itemized trucking cost-per-mile + minimum profitable rate.
| Name | Required | Description | Default |
|---|---|---|---|
| mpg | No | ||
| other_mo | No | ||
| tires_cpm | No | ||
| x_payment | No | ||
| fuel_price | No | ||
| annual_miles | Yes | ||
| insurance_mo | No | ||
| maintenance_cpm | No | ||
| truck_payment_mo | 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, the description carries the full burden, but it only mentions a $0.09 x402 payment and the output categories. It does not disclose how the calculation works, whether external data is needed, authentication requirements, or failure behavior. The 'via x402' hints at a payment mechanism but adds little 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 extremely brief and front-loaded with price and purpose, containing no filler. While the structure is minimal, it earns its place by communicating the key deliverable and cost in one short phrase.
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 9 parameters, no output schema, and no annotations, a single sentence is drastically insufficient. Missing are parameter definitions, expected input format, calculation logic, and return value details, making the description inadequate for reliable invocation.
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 0%, and the description does not name or explain any of the 9 parameters. Fields like annual_miles, mpg, and other_mo are left entirely to name inference, with no units, defaults, or relationships described.
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 produces an itemized trucking cost-per-mile and minimum profitable rate, which conveys the core purpose. It lacks an explicit verb like 'calculate' but the deliverable is specific. It does not directly distinguish itself from the sibling truck_load_profit, though the cost-per-mile focus is distinctive enough.
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 alternatives. Sibling tools like truck_load_profit exist but are not mentioned, and there are no context signals, prerequisites, or exclusions provided.
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.