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Glama

Htmltext

by io.github.pipeworx-io

Extract Metadata

extract_metadata
Read-onlyIdempotent

Pull page metadata from HTML (keyless, offline): , meta description/keywords, and Open Graph (og:*) tags.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
htmlYesThe HTML.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "html": "<html><head><title>My Blog</title><meta name=\"description\" content=\"A personal blog about web development\"><meta property=\"og:image\" content=\"https://example.com/og-image.jpg\"></head></html>"
+  },
+  {
+    "html": "<head><title>Product Page</title><meta name=\"keywords\" content=\"electronics, gadgets\"><meta property=\"og:title\" content=\"Latest Gadget\"></head>"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description adds context beyond annotations by specifying the tool is offline, keyless, and extracts specific metadata. Annotations already indicate read-only, idempotent, non-destructive behavior. No contradictions.

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 one sentence, no wasted words, and front-loaded with the action verb. It efficiently conveys the tool's purpose and scope.

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?

For a simple tool with one parameter, the description covers the main functionality and output types. It could mention the output format, but the purpose is clear given the tool name and sibling context.

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 a simple string parameter. The description does not add significant meaning to the parameter beyond what the schema provides. Baseline 3 is appropriate.

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 extracts specific metadata types (title, meta description/keywords, Open Graph tags) from HTML. It distinguishes from sibling tools like extract_links and html_to_text, which have different purposes.

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 implies the tool works offline and without keys, which guides usage context. However, it does not explicitly state when to use or not use this tool versus alternatives, though the specificity of metadata extraction is clear.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping or nested roles: ask_pipeworx_beta is explicitly identical to ask_pipeworx currently, ask_pipeworx_grounded uses the same router, and polymarket_arbitrage/polymarket_edges both surface mispricings. ai_visibility_check and scan_competitor_ai_presence are also tightly coupled, making tool selection error-prone.

Naming Consistency3/5

Names are uniformly snake_case and mostly readable, but the pattern is mixed: verb-first names like extract_links and discover_tools coexist with noun-first product names like polymarket_edges and entity_profile, plus bare verbs like remember and forget. This breaks the predictable verb_noun convention.

Tool Count2/5

34 tools is far too many for a server named Htmltext, and most tools are unrelated to HTML processing. Even as a broad data-research server, the count exceeds the usual 3-15 sweet spot and includes meta-tools, near-duplicate query modes, and niche utilities that bloat the surface.

Completeness3/5

The set is unusually broad—covering data research, prediction markets, memory, subscriptions, HTML extraction, AI visibility, and package scanning—but no single domain is fully fleshed out. HTML tools only do extraction, prediction-market tools lack a simple market browser, and there is no general web fetch tool. Most gaps can be worked around via ask_pipeworx, but the surface feels like a grab bag rather than a cohesive lifecycle.

Resources