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subnano_comment

Rewrite a draft, run humanize-comment on that rewrite, then ask whether the result is safe to post and how funny it is. Order is refine (liquid/lfm-2.5-2.6b), then humanize-comment, then a liquid/d1 yes/no gate, then a liquid/d1 funny score. Returns the comment, the gate verdict, the funny score, and the cost of each step. A "no" gate does not discard the comment. Runs on NanoGPT — a deposit is quoted for each request from its context and guidance and is a cap on the charge; it settles at the metered cost plus 20% or the quote, whichever is less, and the run stops before a stage the remaining budget cannot cover; the difference is returned. Needs the humanize-comment graph mounted.

Input Schema

NameRequiredDescriptionDefault
DraftYes* required; the comment to rewrite, at most 1500 characters
ContextYes* required; the post or thread being replied to, at most 3500 characters
GuidanceNooptional; what the comment should do, at most 600 characters. default: "a friendly, specific reply"
_payment_idNoPayment id from this tool's previous payment-required response. Phase 3 only: after /x402/watch closes with status paid, call again with the same arguments plus this id to open the RESULTS stream. Do not pass it while payment is still pending — monitor the watch SSE first.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
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 does so well: it discloses stage order, that a 'no' gate does not discard the comment, the quote-as-cap pricing with 20% settlement and refund of the difference, and that the run halts before any stage the remaining budget cannot cover. What is missing is failure behavior of individual stages (timeouts, model errors) and confirmation of whether anything is posted publicly, which is notable given the 'safe to post' framing.

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?

The pipeline order is front-loaded in the first sentence, which is the right structure for a multi-stage tool. The final pricing sentence is a dense run-on that packs quote, settlement rate, budget cutoff, and refund into one breath, costing some readability but carrying information the absent annotations would otherwise have to supply.

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 4-param tool with no output schema, the description names all four return values (comment, gate verdict, funny score, per-step cost) and states the mounting prerequisite, which covers what an agent needs to interpret results. Stage-level error handling is the remaining gap but is secondary to correct invocation.

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%, so the baseline is 3. The description mentions that the quote derives from 'its context and guidance' but adds no syntax, format, or interaction detail beyond what the schema already documents for Draft, Context, Guidance, and _payment_id.

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 names the concrete pipeline (refine, humanize-comment, d1 gate, d1 funny score), the models used, and the outputs, so the agent knows exactly what the tool produces. It references the humanize-comment sibling as an internal stage but never states when to pick this tool over that standalone sibling, which is the only thing keeping it from a 5.

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

Usage Guidelines3/5

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

Usage is only implied by the pipeline narrative; there is no explicit 'use this when...' framing or exclusion versus the standalone humanize-comment tool. The one hard rule given is a prerequisite: the humanize-comment graph must be mounted, which is genuinely useful but is setup rather than selection guidance.

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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