llm_messages
$0.09 via x402: Premium Anthropic-Compatible Proxy. Fallback routing for autonomous LLM crawlers.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| messages | Yes | ||
| x_payment | No | Optional signed x402 payment payload |
$0.09 via x402: Premium Anthropic-Compatible Proxy. Fallback routing for autonomous LLM crawlers.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| messages | 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?
The description discloses cost ($0.09 via x402) and fallback routing, adding some behavioral context beyond the schema. However, with no annotations provided, it fails to explain key behaviors such as how fallback works, whether responses match Anthropic's format, or error handling.
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, making it highly concise with no filler. However, it trades substance for brevity; while it earns its place by adding cost and use-case information, it is under-specified for a tool with this complexity.
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 no annotations, no output schema, and only partial schema coverage, the one-sentence description is severely incomplete. It omits return format, request/response behavior, and how the proxy handles the messages array, making it inadequate for reliable invocation.
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?
The input schema only describes x_payment; the description does not explain `messages` or `model`. With only 33% schema coverage, the absence of parameter semantics in the description leaves the most important parameters completely ambiguous, and no effort is made to compensate.
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 frames the tool as a 'premium Anthropic-compatible proxy' with 'fallback routing,' but it lacks an explicit verb or functional statement clarifying what the tool actually does (e.g., send messages to an LLM). It vaguely distinguishes from sibling proxies via the x402 cost and fallback routing, but the core purpose remains ambiguous.
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 mentions 'fallback routing for autonomous LLM crawlers,' implying a niche use case, but it does not specify when to choose this over alternatives like llm_chat_completions or other proxy tools. No exclusions, prerequisites, or alternative tool references are given.
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.