destilar-tables
destilar specialized for tables [x402: 0.01 USDC on Base, pay-per-use]
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | pipeline input |
Andreax x402 Services
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It adds useful cost/pricing context via 'x402: 0.01 USDC on Base, pay-per-use,' but says nothing about input handling, output format, side effects, or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and front-loaded: the key qualifier 'tables' appears immediately, and the pricing detail is cleanly bracketed at the end. It contains no wasted words, though it is terse to the point of under-specification.
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?
With only one vague string parameter, no output schema, no annotations, and many closely related siblings, the description leaves out essential selection and invocation details. An agent cannot tell what output to expect or how this differs from destilar, destilar-structured-json, or vision-tables.
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?
The input schema has 100% coverage for its single parameter, but the schema description is only the generic 'pipeline input.' The tool description adds the context that the input should relate to tables, which is helpful but still leaves the actual expected format unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description only restates the tool's name as 'destilar specialized for tables' without stating a concrete action, input, or output. It signals the table domain but does not explain what distilling tables means or what an agent should expect the tool to do.
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 'specialized for tables' implies the tool should be used when the input involves tables, but there is no explicit guidance, exclusion, or mention of alternatives. With siblings like destilar, destilar-structured-json, vision-tables, and ocr-tables, the description does not help an agent choose between them.
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.