$0.09 via x402: freight load profitability — profit, true CPM, break-even rate, ACCEPT/REJECT verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| mpg | No | ||
| rate | Yes | ||
| miles | Yes | ||
| deadhead | No | ||
| x_payment | No | ||
| fuel_price | No | ||
| cost_per_mile_fixed | No | ||
| cost_per_mile_variable | 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 partially carries the burden by naming four outputs (profit, true CPM, break-even rate, verdict). This gives some sense of behavior, but it does not disclose whether it is a pure calculation, what assumptions are made, or any limitations. It adds basic output context but lacks depth.
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 sentence and therefore concise, but the leading '$0.09 via x402' is non-functional metadata that delays the actual purpose. Once past that, the output list is efficient, but the front-loading is not ideal.
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?
Given 8 parameters, no output schema, and no annotations, the description is insufficient for an agent to know what inputs to provide or what the full response structure looks like. It names four output concepts but doesn't connect them to input parameters or describe the verdict logic.
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 0%, and the description does not explain any of the 8 parameters. Parameter names like deadhead and x_payment are left undefined, and the description only vaguely relates to rate/miles via the concept of CPM. The description fails to compensate for the missing schema information.
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 indicates the tool computes freight load profitability, naming specific outputs (profit, true CPM, break-even rate, ACCEPT/REJECT verdict). It distinguishes itself from the sibling truck_cost_per_mile by focusing on profitability rather than cost-only. However, it lacks an explicit verb like 'calculate' and opens with irrelevant payment metadata.
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?
No guidance is given on when to use this tool versus alternatives or how the ACCEPT/REJECT verdict should be interpreted. There is no mention of prerequisites, context, or exclusions, leaving the agent without decision criteria for selection.
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.