Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web content as LLM context.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | HTML string to convert | |
| strip_links | No | Strip link URLs, keep text only (default: false) |
| Name | Required | Description | Default |
|---|---|---|---|
| markdown | No | ||
| markdown_length | No | ||
| original_length | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite annotations already declaring readOnly, idempotent, and non-destructive, the description adds valuable behavioral detail: it strips scripts, styles, nav, ads, and comments, and converts specific HTML elements. This goes beyond annotations and gives the agent a clear mental model of the tool's output.
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: the first states the primary action, the second lists what is stripped and converted, ending with a use case. Every word contributes, with no wasted space or 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?
The tool is moderate complexity, but with an output schema present and clear annotations, the description covers all essential aspects: input, transformations, removals, and ideal usage context. No critical gaps remain for an agent to use the tool correctly.
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 both 'input' and 'strip_links' already described in the schema. The description mentions links in the conversion list, which slightly reinforces the strip_links option, but it adds no new parametric meaning beyond the schema. Baseline 3 is appropriate given the complete schema coverage.
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 starts with a specific verb+resource: 'Convert HTML to clean Markdown.' It then enumerates the transformations (headings, lists, links, images, code blocks) and removals (scripts, styles, nav, ads, comments), making the tool's function unmistakable and distinguishing it from siblings like strip_markdown or escape_html.
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 phrase 'Ideal for preparing web content as LLM context' provides clear context for when to use the tool, but it does not explicitly mention exclusions or alternatives. Since siblings like strip_markdown or extract_links exist, a brief 'when-not' statement would elevate this, but the context is adequate.
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.
Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.
The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.
With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.
The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.