bulk_draft_replies

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

With no annotations, the description discloses key behaviors: it writes to a local file, uses a local LLM, and outputs JSON with action='pending'. It does not detail error handling or file overwriting, but it is sufficiently transparent for the intended use.

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

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

The description is highly concise, using a single sentence plus a brief note on output and workflow. Every word serves a purpose with no redundancy.

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

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

Given the tool's simplicity (3 parameters, no output schema, no annotations), the description is largely complete. It covers the core function, output format, and next steps. Missing error behavior, but overall adequate.

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

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

The input schema has 0% description coverage, so the description must compensate. It implicitly covers post_id (via 'every response on a post') and out (via output file default), but does not mention the model parameter. This partial coverage justifies a score of 3.

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

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

The description clearly states the tool's purpose: it writes to a local file, generates reply drafts for every response on a post using a local LLM, and specifies the output format. It distinguishes itself from sibling tools like send_approved_drafts by outlining the workflow.

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

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

The description provides clear context: it is a local operation ('no Medium call'), and it instructs the user to edit the output then run send_approved_drafts. This implicitly defines when to use it, though it does not explicitly list when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.