vertex_proxy
$0.09 via x402: Premium Google Vertex AI Fallback Node. Fallback routing for Google Vertex AI instances.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| instances | Yes | ||
| x_payment | No | Optional signed x402 payment payload |
Riley Craig x402 Agent Store
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
- Removed
- Added
- Removed
- Added
TDQS
C2.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds some behavioral context: it discloses a cost of $0.09 via x402 and a fallback routing behavior. However, it does not reveal response formats, error handling, permission requirements, or the exact semantics of 'instances,' leaving significant gaps in an agent's understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short, with two sentences and no filler. It front-loads the cost and then states the purpose, which is efficient. The phrasing is a bit cryptic ('$0.09 via x402' and 'Premium...Fallback Node') but still concise and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description should clarify return values and behavioral details. It merely states the tool is a fallback router and does not mention what the response contains, how instances are used, or any error conditions. Given the tool's proxy nature and two parameters, the description is incomplete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes x_payment but leaves the required 'instances' parameter undocumented. The description mentions 'Google Vertex AI instances,' which hints at the parameter's domain but gives no format, allowed values, or request mapping. With 50% schema coverage, the description insufficiently compensates for the undocumented parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool performs 'Fallback routing for Google Vertex AI instances,' clearly identifying the resource and action. It is distinguished from sibling proxies by naming Google Vertex AI, though the term 'fallback routing' is somewhat vague and could be more specific about the nature of the requests it forwards.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The word 'Fallback' implies a backup use case, but the description does not state when to prefer this over direct Vertex AI access or other proxies like bedrock_proxy, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
C2.7/5.0
Disambiguation2/5
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.
Naming Consistency2/5
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.
Tool Count1/5
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.
Completeness3/5
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.