Pay $0.10 USDC on Base to timestamp and SHA-256 a JSON payload. No account, no API key. Returns { stamped, sha256, timestamp_utc, payload_bytes, receipt_id }. Body: { payload }.
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | 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?
With no annotations, the description must cover behavioral traits. It discloses the cost ($0.10 USDC), payment mechanism, and the shape of the return value. However, it does not explain what happens to the payload (e.g., if it is stored, deleted, or if any side effects occur), and the 'Body: { payload }' line introduces ambiguity regarding the actual request structure.
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 concise: two sentences plus a body note. It front-loads the purpose and cost, then lists the return fields. Every word earns its place, and there is no padding.
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 with one parameter and no output schema, the description covers the essential context: pricing, required payment, return values, and the payload requirement. However, the ambiguity around the body vs. the x_payment parameter prevents a perfect score. Overall, it is complete enough for an agent to attempt the call, but with some risk of misinterpreting the request format.
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?
While the schema provides 100% coverage for the single parameter x_payment, the description adds confusing extra info with 'Body: { payload }' which seems to contradict the schema that only lists x_payment. This does not clarify the parameter semantics and may mislead an agent about how to structure the request.
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 action (timestamp and SHA-256 a JSON payload) and the resource (a paid notary service on Base). It distinguishes itself from sibling tools by being the only one performing this specific function, and it includes payment details.
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 implies usage (pay and submit payload) but provides no explicit guidance on when to use this tool versus alternatives. It does mention 'No account, no API key' which lowers the barrier, but there is no direct comparison or exclusion of other tools.
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.