$0.09 via x402: Premium Salesforce Agentforce Fallback Node. Fallback routing for Salesforce Agentforce instances.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | ||
| x_payment | No | Optional 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?
No annotations are provided, so the description carries the full burden. It discloses the cost ($0.09 via x402), which is useful, but it does not explain the tool's behavior: whether it sends external requests, what the response looks like, whether a valid payment payload is required, or what side effects occur. This is a significant transparency gap.
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 very short and contains no filler, but it under-specifies. The opening phrase 'Premium Salesforce Agentforce Fallback Node' is a title-like restatement, while the actual functional value ('Fallback routing for Salesforce Agentforce instances') is one clause. It is concise but at the cost of needed detail.
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 2-parameter tool with no output schema and no annotations, the description should explain return values, expected input semantics, and usage context. It only provides a cost note and a vague routing statement. The lack of any information about the response format or error conditions makes it incomplete for an agent to select and invoke it confidently.
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 only 50% (x_payment is documented, message is not). The description adds nothing about the parameters—neither 'message' nor 'x_payment' is elaborated. Since the description is the only place to clarify the undocumented 'message' parameter, the lack of any specification fails to compensate for the schema gap.
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 identifies a specific verb ('fallback routing') and resource ('Salesforce Agentforce instances'), which distinguishes it from sibling proxy tools like bedrock_proxy and vertex_proxy. However, 'Premium Salesforce Agentforce Fallback Node' reads as marketing language and does not clarify what fallback routing actually does operationally.
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. The phrase 'Fallback routing' implies a failover use case, but there is no explicit context, prerequisites, or exclusions. Sibling tool names (e.g., bedrock_proxy, vertex_proxy) suggest similar proxy patterns, but the description does not differentiate usage scenarios.
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.