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anki-workflow-mcp

anki-workflow-mcp

by Gugart43
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Table From Markdown

table_from_markdown

Convert GitHub-style markdown tables in text—including raw or escaped pipes and newline or separators—into HTML tables, returning status, converted table count, and diagnostics.

Instructions

Convert GitHub-style markdown tables in text (with raw or escaped pipes, separated by newlines or ) into HTML tables. Returns {status: 'PASS'|'FAIL', text: str, tables_converted: int, diagnostics: list}.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "table_from_markdown"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does disclose useful behavior: accepted pipe escaping, newline and <br> separators, and a return shape with status and diagnostics. It is silent on failure conditions, whether non-table text is preserved, whether multiple tables are handled (only hinted at by tables_converted), and whether the input is mutated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose, with the input format and return shape following. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter pure transform with an output schema, the description is nearly sufficient: it defines the input dialect and sketches the return value. The remaining gap is failure behavior, which diagnostics hints at but does not explain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% for the single 'text' parameter, so the description must compensate, and it does: it specifies GitHub-style markdown, raw or escaped pipes, and newline versus <br> row separation. It stops short of clarifying multi-table input or treatment of surrounding non-table content.

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?

The description states a specific verb and resource ('Convert GitHub-style markdown tables ... into HTML tables') and pins down the accepted input dialect. It does not, however, distinguish this from the sibling table_build, which an agent could plausibly confuse with it.

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?

There is no explicit when-to-use or when-not-to-use guidance and no named alternative. The appropriate use case is only implied by the word 'Convert' and could be inferred, but nothing routes the agent between this and table_build.

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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