Return an LLM (Kimi/DeepSeek) answer to your query. General-purpose, pay-per-call inference for when an agent needs a model response. input=your query as text. [x402: 0.005 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Consulta/prompt para el modelo |
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?
With no annotations at all, the description carries the full burden and discloses a meaningful non-obvious behavioral trait: pay-per-call pricing at 0.005 USDC on Base. It also names the underlying models (Kimi/DeepSeek), which helps an agent predict behavior. It could go further on rate limits or failure behavior, but it provides substantial transparency beyond the bare operation.
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?
Two short sentences plus a brief cost note. Every phrase earns its place: purpose, general applicability, input characterization, and pricing. The key action is front-loaded, and the cost information is compactly appended without verbose explanation.
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 one-parameter, no-nested-schema tool with no output schema, the description conveys purpose, appropriate usage context, input format, model family, and monetary cost. Nothing necessary for a basic correct invocation is missing, though no behavior for errors or edge cases is documented.
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 description coverage is 100%, so the schema already explains the single required parameter. The description's line "input=your query as text" adds slight English clarification over the Spanish schema text but does not provide materially new meaning. This matches the baseline for high schema coverage.
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 opens with a concrete verb and resource phrase — "Return an LLM (Kimi/DeepSeek) answer to your query" — which makes the tool's core purpose obvious. It also adds "General-purpose" to position it against specialized sibling tools, though it does not explicitly name or distinguish a direct alternative such as ai-inference.
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?
"for when an agent needs a model response" gives a reasonable usage context and implies pairing with some alternate tool, but it never states when not to use this tool or points to a specific alternative among the many siblings. The guidance is present but mostly implicit.
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.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.