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Quinn's Conveyance — Paid ComfyUI Knowledge MCP

by io.github.jkomalleyjr
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check_dead_end

Check whether an approach to ComfyUI video generation is a known dead end before spending GPU time on it. Returns the failed approach, what was tested, and what to do instead. Use BEFORE running an experiment you're unsure about. Covers WanVideoWrapper, chaining, interpolation, and upscaling. Costs $0.005 per call, paid in USDC on Base via x402.

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": {
      "query": "<query>"
    },
    "name": "check_dead_end"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesYour error message, question, or topic.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and delivers important operational context: the return contents (failed approach, what was tested, what to do instead), the coverage scope, and a non-obvious cost of $0.005 per call paid in USDC on Base via x402. It omits idempotency/rate-limit behavior, keeping it just short of a 5.

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?

Four compact sentences, each earning its place: purpose, return shape, when-to-use, coverage, and pricing. The most decision-relevant info (what it does and when to call it) is front-loaded.

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

Completeness5/5

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

There is no output schema, yet the description explains exactly what comes back (failed approach, what was tested, the recommended alternative), plus coverage domains and the payment model. Nothing an agent needs to decide or call correctly is missing.

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?

Schema description coverage is 100% and the single 'query' parameter is documented in the schema itself. The description adds framing ('your error message, question, or topic' is not restated), so this is the baseline 3 where structured data does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (check) and resource (dead end for a ComfyUI video approach) with the domain of coverage enumerated (WanVideoWrapper, chaining, interpolation, upscaling). An agent can distinguish this registry-lookup tool from siblings like diagnose_comfyui_ops and get_chaining_rule without reading any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use BEFORE running an experiment you're unsure about,' giving a clear triggering context. However it names no alternatives or exclusions, so the agent must infer when diagnose_comfyui_ops would be the better pick instead.

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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