html_to_textConvert HTML to readable plain text (keyless, offline): drops scripts/styles/comments, converts block elements to newlines, decodes entities, and collapses whitespace. Ideal for cleaning scraped HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | The HTML to convert. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "html": "<html><body><h1>Welcome</h1><p>This is a <strong>test</strong> page.</p><script>console.log('hidden');</script></body></html>"
+ },
+ {
+ "html": "<div><p>First paragraph</p><p>Second paragraph</p></div><footer>Copyright 2024</footer>"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses behavioral details beyond annotations (e.g., dropping scripts/styles, converting block elements) without contradicting annotations. Annotations already indicate safe, idempotent operation, and the description adds specific transformation rules.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the main action and then detailing specifics. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 and clear annotations, the description fully covers its purpose, transformations, and ideal use case. No output schema is needed as return value is straightforward.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter 'html' described as 'The HTML to convert.' The description does not add extra meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts HTML to plain text, listing specific transformations (drops scripts/styles/comments, converts block elements to newlines, decodes entities, collapses whitespace). It distinguishes itself from sibling tools by focusing on a specific utility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context by noting it is 'keyless, offline' and 'ideal for cleaning scraped HTML,' implying when to use it. However, it does not explicitly state when not to use it or provide alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.