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Glama

Stringer CleanExtract

by app.getstringer

clean_extract

Extract clean, token-dense markdown from a public URL or raw HTML string, with extraction_status and detailed extraction_quality metadata that identifies empty or probably partial output. The first 3 calls per source IP are free, total, with no header or signup needed; the allowance does not reset. Every later extraction costs USD 0.05 via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
url_or_htmlYesA public HTTP(S) URL or a raw HTML string to convert into Markdown.
max_output_bytesNoOptional maximum UTF-8 byte length of the returned Markdown.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / max_output_bytes
      Added value: +{
+  "description": "Optional maximum UTF-8 byte length of the returned Markdown.",
+  "maximum": 1048576,
+  "minimum": 1,
+  "type": "integer"
+}
  2. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • changedInput schema / properties / url_or_html / description
      Previous value: -"The public URL or raw HTML string to convert into markdown."New value: +"A public HTTP(S) URL or a raw HTML string to convert into Markdown."
    • addedInput schema / properties / url_or_html / minLength
      Added value: +1
  3. First observed

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 and does well: it discloses output quality metadata, indicates that empty or partial output may be flagged, and explicitly describes the free allowance and per-call cost. It does not detail error behavior or exact response structure, but the core behavioral traits are covered.

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?

Two sentences carry the entire message with zero filler. The primary purpose is front-loaded, followed by the most decision-relevant operational detail (pricing/free allowance). Every clause earns its place.

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 no output schema and no annotations, the description supplies the key context agents need: input type, output nature, quality metadata, and cost model. It could go slightly deeper on response shape or error handling, but for a 2-parameter extraction tool it is largely complete.

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 the baseline is 3. The description echoes the meaning of the first parameter (public URL or raw HTML) but adds no new meaning beyond the schema. The optional max_output_bytes parameter is not mentioned, though its schema description already explains 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 names a specific verb ('Extract'), a concrete resource type ('public URL or raw HTML string'), and the exact expected output ('clean, token-dense markdown') plus return metadata. It fully clarifies what the tool does, even without sibling tools to differentiate from.

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?

It clearly states when this tool is appropriate: converting public URLs or raw HTML to Markdown. It also gives practical usage context—first 3 calls free, later calls paid—which helps an agent decide whether to call it repeatedly. It does not mention alternatives, but there are no sibling tools to contrast against.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Stringer CleanExtract toolsInspect
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TDQS

A4.3/5.0
Disambiguation5/5

There is only one tool, so no agent could confuse it with another. The single operation has a clear and narrow boundary.

Naming Consistency5/5

The tool name 'clean_extract' follows a clear verb_noun pattern and accurately describes what the server does. With one tool, the naming is predictable and unambiguous.

Tool Count3/5

One tool is minimal and feels thin, even if the server's extraction purpose is narrow. It is not bloated, but it falls below the typical well-scoped 3-15 tool range.

Completeness5/5

The tool accepts both a public URL and raw HTML, returns extraction status and quality metadata, and addresses the full stated purpose of clean extraction. There are no obvious missing operations for this focused service.

Resources