text_html_to_markdownConvert HTML to clean Markdown. Use when extracting readable content from web pages or migrating HTML docs to Markdown format.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | HTML string to convert to Markdown |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already indicates a safe non-destructive operation, so the bar is lower. The description adds 'clean Markdown' and 'extracting readable content,' which implies output quality, but does not disclose details about handling malformed HTML, lossy conversion, or preservation of specific elements. It provides some context beyond annotations but not rich behavioral detail.
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-loaded with the core action and then usage guidance. There is no fluff or repetition, making it highly concise and well-structured.
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 one-parameter tool with no output schema, the description covers the purpose, usage scenarios, and implied output format ('Markdown'). It does not mention edge cases or limitations, but the simplicity of the tool and the readOnlyHint annotation make the description adequately complete for an agent to select and invoke it.
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% for the only parameter 'html' with a clear description. The tool description does not add further parameter-specific meaning beyond restating the conversion, so it meets the baseline for high schema coverage but provides no additional value.
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 uses a specific verb and resource: 'Convert HTML to clean Markdown.' It clearly states the conversion direction, distinguishing it from the sibling tool text_markdown_to_html, and adds context with 'extracting readable content from web pages.'
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 explicitly says 'Use when extracting readable content from web pages or migrating HTML docs to Markdown format.' This gives clear context and use cases, though it does not mention when not to use it or explicitly name alternatives like text_markdown_to_html.
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.
The tools are grouped into clear categories (dev, lookup, security, text, transform), which helps with disambiguation, but within categories there is some overlap. For example, lookup_ssl and lookup_ssl_cert_expiry both handle SSL certificates, and dev_url_encode/dev_url_decode are closely related but distinct. Most tools have unique purposes, but a few could be confused without careful reading of descriptions.
The naming follows a consistent snake_case pattern with a clear prefix structure (dev_, lookup_, security_, text_, transform_), which aids in organization. However, there are minor deviations like dev_cron_describe using 'describe' while others use verbs like 'generate' or 'convert', and some tools have longer names that break the verb_noun pattern slightly. Overall, the naming is predictable and readable.
With 49 tools, the count is excessive for a utility server, making it overwhelming and likely to cause confusion or inefficiency. While the tools cover many use cases, a more focused set of 15-25 tools would be more manageable and better scoped. The high number suggests feature bloat rather than a coherent, minimal surface.
The tool set is highly complete for its utility and development support domain, covering a wide range of operations from data transformation and security to lookups and text processing. There are no obvious gaps; each category provides comprehensive coverage, such as full text encoding/decoding, security functions, and various lookup capabilities, ensuring agents can handle diverse tasks without dead ends.