json_to_markdown_table
Convert list of dicts or list of lists to Markdown table. Returns: {table, rows, columns}
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| headers | No |
Htmlpdf Transform Mcp
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Minimal behavioral disclosure; only mentions conversion and return fields. No annotations provided, so description should cover edge cases or side effects, but it does not.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise; essential information front-loaded. Every sentence adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple conversion tool with 2 params and no output schema, description provides basic functionality and return structure. Lacks examples or handling of edge cases, but sufficient for straightforward use.
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?
Description partially covers `data` parameter by specifying acceptable types (list of dicts or lists), but `headers` parameter is not explained. With 0% schema coverage, description partially compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool converts lists to Markdown table. Distinguishes from sibling tools focused on HTML processing, but lacks elaboration on the output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use or not use this tool versus alternatives. Missing context on prerequisites or constraints.
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
B3.3/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: extracting tables, health check, converting HTML to markdown or text, and converting JSON to markdown table. No functional overlap.
Naming Consistency4/5
Most tools follow a pattern (source_to_target or verb_noun), and all use snake_case. One exception: 'health_check' is a noun_noun instead of verb_noun, but it's a minor deviation.
Tool Count4/5
With 5 tools, the count is reasonable for a focused server. It covers core HTML transformations, though PDF tools are missing from the name 'Htmlpdf'.
Completeness2/5
The server name implies both HTML and PDF handling, but there are no PDF-related tools. Significant gaps exist in the stated domain, limiting agents' ability to perform PDF operations.