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codeiva11
by codeiva11

extract_data

Extract structured data from any web page using multiple modes like regex, JSON, meta, and links. Choose the method that matches your data format.

Instructions

Extract structured data from the current page in multiple modes: regex, json, meta, structured, auto, apiDiscovery, parse (string conversion), transform (data format conversion), or links (all links including nested iframes).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoauto
flagsNoRegex flagsgi
typesNoFor meta mode: which tag groups to include (all, meta, og, twitter)
sourceNoall
patternNoFor regex mode: the regular expression pattern
jsonPathNoFor json mode: JSONPath expression
selectorNoFor structured/links mode: CSS selector to scope the extraction
inputDataNoFor transform mode: the string to convert
keyOffsetNoFor transform mode: offset value (optional)
autoDecodeNoAutomatically process Base64 or percent-encoded values in results
transformKeyNoFor transform mode: optional conversion parameter
autoDetectKeyNoFor transform mode: locate the conversion parameter from page scripts automatically
includeHiddenNoFor links mode: include hidden/non-visible links
searchIframesNoFor links mode: search inside embedded frames

Schema Changelog

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

  1. First observedv3.2.13

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does add useful scope information: extraction is from the current page, and links mode includes nested iframes. However, it does not disclose return behavior, auto-mode behavior, potential side effects, or whether any network/API calls are triggered by modes like apiDiscovery.

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 a single, dense sentence with the core action front-loaded and the mode list following. It is efficient and contains no filler, though the long comma-separated list is somewhat heavy and could benefit from clearer grouping or line breaks.

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?

This is a complex 14-parameter tool with no output schema and no annotations, so the description must compensate. It provides a useful mode inventory but does not explain mode-to-parameter mapping, default behavior, return values, or failure modes. An agent would likely need to inspect the schema carefully and still be uncertain about how modes like apiDiscovery or transform behave end-to-end.

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

Parameters4/5

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

Schema description coverage is high at 86%, so the baseline is 3. The description adds value beyond the schema by defining ambiguous mode names: parse is 'string conversion,' transform is 'data format conversion,' and links mode explicitly covers nested iframes. This helps an agent understand the enumeration beyond raw parameter metadata.

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 a clear verb and resource: 'Extract structured data from the current page,' and enumerates distinct extraction modes (regex, json, meta, structured, etc.). It does not explicitly differentiate from sibling tools like get_content or deep_analysis, but the mode list gives enough specificity that the purpose is not ambiguous.

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?

The description gives no guidance on when to use this tool versus alternatives like get_content, api_analyzer, or media_extractor. It does not mention prerequisites, recommended modes for common scenarios, or exclusions. The only implicit usage signal is that it operates on the current page.

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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