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MeshMarket

Ride-Along Reply (experimental)

ride-along-reply

Ride-Along Reply (experimental) — Experimental reply or quote-post planner for borrowing reach from a larger account. Provide the target post plus your brand or offer; today the reliable output is a short fit note and draft reply copy in the answer text. You still approve and post it yourself. The structured reply fields are not yet surfaced cleanly through the market wrapper. (3 MESH/call, a tool · marketing)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesCapability-specific payload, e.g. agent-brain: {think:'...'}; agent-memory: {action:'store'|'recall', content|query}

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description is unusually candid: it labels the tool experimental, specifies that the reliable output is a short fit note and draft reply copy in the answer text, warns that structured reply fields are not yet surfaced cleanly, and clarifies that the user still approves and posts. It also discloses the cost. This adds substantial behavioral context beyond the annotations.

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 description is longer than average but each section contributes: purpose, required inputs, output format, user responsibility, wrapper limitation, and cost. The opening repeats the title somewhat and the trailing marketing metadata is minor clutter, but overall it is efficiently organized.

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 an experimental tool with no output schema and a generic input schema, the description covers the essential input requirements, the output the agent can rely on, and the caveat about structured fields. It is not a 5 because it leaves the exact input representation of 'target post' and 'brand or offer' ambiguous.

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?

The single `input` parameter's schema description is generic and not tailored to this tool. The prose description adds semantic meaning by saying the input must contain the target post plus the brand or offer, but it does not specify the exact object shape, key names, or how to encode the target post, so it only partially compensates.

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 opens with a specific verb and resource: a reply or quote-post planner for borrowing reach from a larger account. This makes it distinct from broader posting tools and marketing planners in the sibling list, and 'You still approve and post it yourself' reinforces that it is a planning aid, not an actual posting action.

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?

It clearly states the intended context: use when you want to reply or quote-post to leverage a larger account's reach. It tells the user to provide the target post plus brand or offer, but it does not name alternatives or state when not to use this tool, so it stops short of a 5.

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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