text_markdown_to_htmlConvert Markdown text to HTML. Use when rendering Markdown content for web display or email templates. Returns sanitized HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| gfm | No | Enable GitHub Flavored Markdown | |
| markdown | Yes | Markdown source text | |
| sanitize | No | Strip script tags, event handlers, iframes, objects, and embeds |
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 description claims 'Returns sanitized HTML,' but the input schema shows sanitize defaults to false, meaning the output is not sanitized by default. This misleading statement is a behavioral inaccuracy. The readOnlyHint annotation is positive, but the description doesn't disclose the gfm default or the effect of the sanitize parameter beyond the schema.
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 three short sentences and front-loads the core purpose. Each sentence serves a function: purpose, usage, and return value. However, the return-value sentence is inaccurate, but that is a matter of correctness rather than conciseness.
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 read-only conversion tool, the description covers purpose, usage, and return type, but it fails to accurately describe the sanitization behavior and omits the gfm option's effect. The schema covers parameter details, so the description is mostly adequate but not fully complete due to the misleading sanitized statement.
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?
The schema provides complete descriptions for all three parameters (markdown, gfm, sanitize) with 100% coverage, so the baseline is 3. The description adds no additional parameter-level semantics; it only mentions the overall conversion and the misleading sanitization claim.
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 opens with 'Convert Markdown text to HTML,' which is a specific verb and resource. It also provides a clear use case ('rendering Markdown content for web display or email templates') and inherently distinguishes itself from sibling tools like text_html_to_markdown by its stated direction of conversion.
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 states 'Use when rendering Markdown content for web display or email templates,' providing a clear context for when to invoke the tool. It does not list exclusions or alternative tools, so it stops short of a full when/when-not comparison.
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.