Computer vision: describe or analyze an image with a LOCAL multimodal model (llava). For agents that need to 'see' (describe scenes, read diagrams, classify images). Upload the image via multipart or as 'archivo_b64'; 'input' = the question or instruction abou [x402: 0.008 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | Qué quieres saber de la imagen | |
| archivo_b64 | Yes | Imagen en base64 (o subir 'archivo' por multipart) |
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?
There are no annotations, so the description carries the full burden for behavior and safety. It discloses that the model is local ('LOCAL multimodal model (llava)'), indicates pay-per-use pricing, and explains the upload mechanism. It does not describe the exact output format or failure behavior, but the most important behavior-relevant details are present.
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 purpose and usage are front-loaded in fairly concise sentences. However, the description contains an embedded, visibly artifact-like fragment '[x402: 0.008 USDC on Base, pay-per-use]' and an unfinished word 'abou', which makes the text look noisy and not fully model-facing.
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?
With only two parameters, a required base64 image, no output schema, and a well-scoped description, this is sufficient for correct invocation in most cases. It tells the agent what to send, how to send it, what the input means, and that the tool is a pay-per-use local vision model. It never explicitly states the return structure, but the core usage context is covered.
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 a baseline of 3 is appropriate. The description adds helpful framing by mapping 'input' to a question or instruction and mentioning multipart upload, but it mostly restates what the schema already documents and does not add format, size, or boundary details.
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 clearly states that the tool 'describes or analyzes an image' with a local multimodal model, which is a specific verb and resource. It also gives concrete use cases like describing scenes, reading diagrams, and classifying images. It does not explicitly contrast itself with nearby siblings like 'ocr' or 'read-pdf', so it stops short of full sibling differentiation.
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 provides clear context for when the tool should be used: 'For agents that need to see' and lists representative tasks. It does not explicitly name alternatives or say when not to use this tool, which would make the guidance stronger, but the intended usage context is fairly clear.
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.