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destilar-batch

destilar specialized for batch [x402: 0.01 USDC on Base, pay-per-use]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYespipeline input

Schema Changelog

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

  1. Added

TDQS

C2.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does disclose the pay-per-use cost and payment rail ('x402: 0.01 USDC on Base'), which is genuinely useful. However, it says nothing about processing semantics, limits, batching format, or result behavior, leaving substantial behavioral information undisclosed.

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

Conciseness2/5

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

The description is a short fragment rather than a complete sentence. While it avoids verbosity, it sacrifices necessary clarity: the core action is undefined and the cost detail takes up most of the space. This is under-specification rather than effective conciseness.

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?

For a tool with no output schema and no annotations, the description is incomplete. It mentions billing and batch mode but omits what 'destilar' produces, what the pipeline input should contain, and what the return value looks like. An agent could invoke it with random text and have no idea what to expect back.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single 'input' parameter is already documented as 'pipeline input', establishing the baseline of 3. The description adds only the 'batch' hint, which may imply the input should contain multiple items, but it does not specify delimiters, array format, or size limits. It neither fully compensates for nor conflicts with the schema.

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 says 'destilar specialized for batch', which identifies the tool as the batch variant of the destilar family and distinguishes it from siblings like destilar, destilar-multi-language, and destilar-structured-json. However, 'destilar' itself is never defined in English, so an agent unfamiliar with the term cannot tell what action the tool performs. It is a recognizable mode label rather than a clear verb+resource statement.

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 given about when to use this tool versus alternatives. 'Specialized for batch' implies it is for batched inputs, but there is no mention of when not to use it, how it differs from destilar-tables or destilar-structured-json, or any prerequisites. The agent has to infer usage from the name alone.

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

C2.6/5.0
Disambiguation1/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness3/5

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.

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