$0.09 via x402: Generate copy-pasteable configuration payloads and IaC templates to connect paid MCP servers to Salesforce Agentforce, Google Vertex AI, and AWS WAF.
| Name | Required | Description | Default |
|---|---|---|---|
| pay_to | No | Target USDC payout address | |
| platform | Yes | Platform target: agentforce | vertex | aws-waf | |
| x_payment | No | Optional signed x402 payment payload | |
| service_url | No | Custom service root URL |
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?
The description adds useful behavioral context: it discloses the cost ($0.09 via x402) and the output nature (copy-pasteable configs). However, without annotations, it does not disclose safety properties, authentication requirements, error behavior, or side effects, leaving significant gaps.
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 that front-loads the cost and purpose, with no redundant text. It efficiently conveys the essential information in a compact, well-structured format.
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?
With no output schema and no annotations, the description should explain expected return values and prerequisites. It states the output is configuration payloads and IaC templates, but lacks details on response structure, error cases, or setup requirements. It is adequate but has clear gaps for a tool with this complexity.
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 100%, so the baseline is 3. The description does not add parameter-specific semantics beyond the schema; the platform targets are already enumerated in the schema's enum, and the description adds no additional detail about parameters like pay_to, x_payment, or service_url.
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 a specific verb ('Generate') and the resource ('configuration payloads and IaC templates'), listing the three target platforms (Salesforce Agentforce, Google Vertex AI, AWS WAF). This distinguishes it from sibling proxy tools like agentforce_proxy and vertex_proxy, which serve a different function.
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 provided for when to use this tool versus alternatives. The description gives a use case but no explicit selection criteria, exclusions, or mention of alternative tools. The agent is left to infer when this tool is appropriate.
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.