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smart-data-extractor

by io.github.lazymac2x

auto_schema_learn

Idempotent · 30s timeout · Automatically infer JSON Schema from sample data without extraction. Pass idempotency_key to deduplicate within 5 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sample_dataYesRepresentative sample data as JSON string (array of objects or single object). Schema is inferred from structure; use first 1-10 rows for array samples. Max 200KB.
idempotency_keyNoOptional cache key (UUID/string) for 5-minute deduplication. Repeat calls with same key return cached inferred schema instantly.

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and succeeds by disclosing key behavioral traits: idempotency, 30-second timeout, 5-minute deduplication window, and the fact that it does not extract data. This is unusually transparent for a tool without annotations.

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

Conciseness5/5

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

The description is concise and front-loaded with key traits (Idempotent, 30s timeout) before stating the core function. The additional sentence about idempotency_key is necessary and directly actionable. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with only two parameters and no output schema or annotations, the description covers purpose, behavioral traits, and parameter guidance. It clearly implies the return value is a JSON Schema, making the tool's behavior complete enough for an agent to use correctly.

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 coverage is 100% with detailed parameter descriptions already provided in the schema. The description adds no new parameter semantics beyond what is already in the schema, such as the idempotency_key purpose, so it meets the baseline without adding extra value.

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

Purpose5/5

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

The description clearly states the tool infers JSON Schema from sample data, with the specific verb 'infer' and resource 'JSON Schema'. It distinguishes from sibling extraction tools via the phrase 'without extraction', making its unique purpose evident.

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

Usage Guidelines4/5

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

The description provides clear context that this tool is for schema inference rather than data extraction, which implies when to use it versus the sibling tools. It also gives explicit guidance on using the idempotency_key for deduplication. However, it does not explicitly name alternatives or state when not to use it.

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

A4/5.0
Disambiguation4/5

The tools are mostly distinct: auto_schema_learn is clearly separate as it only infers schemas, while extract_from_api and extract_from_url differ by source type. batch_extract covers multi-source extraction but could overlap with single-source tools in certain scenarios. No two tools are truly indistinguishable.

Naming Consistency3/5

Naming is a mix of patterns: extract_from_api and extract_from_url share a consistent 'extract_from_X' form, but batch_extract uses an adjective+verb structure, and auto_schema_learn places the verb last. All use snake_case, which is readable, but the lack of a uniform verb_noun pattern is noticeable.

Tool Count5/5

Four tools is an ideal scope for a data extraction server, covering schema inference, single-source extraction from API and URL, and multi-source batch extraction. Each tool has a clear purpose, and the count is neither too thin nor too heavy.

Completeness4/5

The tool surface covers the core domain well: schema learning, extraction from API and URL, and batch extraction from multiple formats on a consistent schema. Minor gaps include no dedicated text extraction tool (though batch handles it) and no explicit output formatting options, but common workflows are fully supported.

Resources