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smart-data-extractor

by io.github.lazymac2x

extract_from_api

Idempotent · 30s timeout · Extract structured data from API response JSON with schema adaptation. Pass idempotency_key to deduplicate within 5 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaNoOptional target JSON Schema (draft-07) for field extraction. If omitted, inferred from content structure. Enforces consistent field extraction across multiple API responses.
contentYesAPI response body as raw JSON string (max 200KB). Can be single object, array of objects, or array of primitives. Automatically parsed and validated.
idempotency_keyNoOptional deduplication key (UUID or unique string) for 5-minute cache. Identical calls return cached result instantly.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently indicates idempotency, a 30s timeout, and a 5-minute deduplication window via `idempotency_key`. These are meaningful behavioral traits beyond what the schema reveals.

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 information ('Idempotent · 30s timeout') followed by the core purpose. Every sentence adds value, with no redundancy or filler.

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

Completeness4/5

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

Given the moderate complexity (3 parameters, no output schema), the description covers the essential purpose and behavioral traits. It could mention the return format or error handling, but the schema already handles parameter details, making the description sufficiently complete for most use cases.

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 each parameter already has a detailed description. The tool description only mentions `idempotency_key` without adding new meaning beyond the schema. Thus, it meets the baseline for high coverage but does not exceed it.

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 states 'Extract structured data from API response JSON with schema adaptation'—a specific verb and resource that clearly defines the tool's function. This differentiates it from siblings like extract_from_url and batch_extract, which target different input sources or processing modes.

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 for use: API response JSON and schema adaptation. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, so it earns a 4 rather than a 5.

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