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api_generate

$0.09 via x402: Premium Ollama-Compatible Proxy. Fallback routing for autonomous LLM crawlers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
promptYes
x_paymentNoOptional signed x402 payment payload

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added
  2. Removed
  3. Added
  4. Removed
  5. Added

TDQS

C2.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the $0.09 cost via x402 and its fallback routing behavior, which is useful context beyond the bare schema. However, it says nothing about request/response behavior, failure modes, or whether payment is required, and with no annotations available that gap is significant.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The one-sentence description is very compact and front-loaded with the price and type, but it sacrifices necessary detail for brevity. It is not a tautology, yet it is too sparse to fully inform the agent about how to invoke the tool.

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?

This is a moderate-complexity proxy tool with 3 parameters and no output schema, yet the description provides no return format, error handling, or concrete usage example. The agent would need to infer too much about the tool's behavior, making the description incomplete.

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?

With only 33% schema description coverage, the description must compensate for missing parameter details, but it only loosely relates 'Ollama-Compatible' to the model parameter and 'x402' to x_payment. The required prompt parameter is entirely unaddressed, leaving its semantics unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies the tool as a 'Premium Ollama-Compatible Proxy' with 'Fallback routing for autonomous LLM crawlers,' but it lacks a clear verb stating the core action (e.g., 'generate completions for given prompts'). The name 'api_generate' and the prompt/model schema imply an LLM generation proxy, but the purpose remains vague compared to siblings like llm_chat_completions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Fallback routing for autonomous LLM crawlers' suggests a specific scenario when to use this tool, but it does not explicitly contrast it with alternatives such as llm_chat_completions or bedrock_proxy. No exclusions or conditions are provided, so usage guidance is implied rather than explicit.

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.