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save_reply

Save a ChatGPT reply to a local file without passing the full text through the model, extract the largest markdown block, and get word and section stats.

Instructions

Write a reply to a file under the work dir without passing the whole text through the model. reply_format largest_markdown extracts the biggest ```markdown block (falls back to full text). Returns word/section stats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chatYes
pathYes
indexNo
reply_formatNolargest_markdown

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

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. It does disclose useful behavior: the largest_markdown extraction rule, fallback to full text, and return of word/section stats. However, it does not say whether an existing file is overwritten or truncated, what happens on invalid paths, or how index affects the saved reply, leaving key side effects of a write operation unstated.

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?

The description is compact and front-loaded: the main verb and resource appear first, then the format option, then the return value. Every sentence carries distinct information and there is no filler.

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

Completeness2/5

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

With four parameters, no output schema, and no annotations, an agent still lacks enough context to call this safely: index semantics and overwrite behavior are absent, and the return stats are only vaguely described as 'word/section stats'. The description is not complete for the complexity of the tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the prose must explain all four parameters. It explains reply_format's values, and path is inferable from 'file under the work dir', but chat is never defined and index's role and default of -1 are not explained at all. This is a meaningful gap for an agent deciding what to pass.

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?

The description names a specific action and object: 'Write a reply to a file under the work dir' and adds detail about the save format. This differentiates it from sibling tools like get_reply and check_file, which read or inspect rather than persist a reply.

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?

It implies a use case ('without passing the whole text through the model' and extracting markdown locally) but never states when to choose save_reply over alternatives or when not to use it. There is no explicit comparison with get_reply, send, or check_file, so the guidance is only implied.

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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