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smart-data-extractor

by io.github.lazymac2x

extract_from_url

Idempotent · 30s timeout · Extract structured data from URL content with auto schema learning. Pass idempotency_key to deduplicate identical calls within 5 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesHTTP(S) URL to fetch (will auto-download and parse), or raw content string (up to 200KB). Max 200KB after fetch.
schemaNoOptional pre-defined JSON Schema (draft-07). If omitted, schema is auto-inferred from content. Provide to enforce strict field extraction and type coercion.
idempotency_keyNoOptional UUID or unique identifier for 5-minute deduplication cache. Same key + tool = cached result in <5ms, zero re-fetching.

TDQS

A3.7/5.0
Behavior4/5

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

The description discloses idempotency, a 30s timeout, and a 5-minute deduplication cache, which are behavioral traits not covered by annotations (none provided). It does not discuss error handling, output format details, or authentication requirements, but the disclosed traits are valuable.

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 two sentences, starting with key traits (idempotent, timeout) and then the purpose. Every sentence adds distinct information; no filler.

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

Completeness3/5

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

The tool has 3 parameters, no output schema, and no annotations. The description covers the core extraction behavior and idempotency context, but does not explain the response format, how raw content strings are handled, or error conditions. This leaves the agent with some ambiguity about the tool's full behavior.

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?

All three parameters have schema descriptions with 100% coverage, so the description adds little beyond the schema. It reinforces that omitting 'schema' triggers auto-learning and that 'idempotency_key' enables deduplication, both already in schema descriptions.

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

Purpose4/5

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

The description states 'Extract structured data from URL content with auto schema learning', which is a specific action on a specific resource. It distinguishes from batch_extract by focusing on a single URL, but does not explicitly name alternative tools. The raw content string option is not mentioned in the description, only in the schema.

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 description provides conditional use of 'idempotency_key' to deduplicate calls, but does not explicitly state when to use this tool over sibling tools like auto_schema_learn, batch_extract, or extract_from_api. The 'auto schema learning' hint implies when to omit 'schema', but no direct alternatives are given.

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