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ProductClank

by com.productclank

AI-review posts for relevance

review_posts
Destructive

Score every discovered post against relevancy rules and delete the irrelevant ones (kept when dry_run:true). Costs 2 credits per post reviewed — dry runs are billed too, since the AI review runs either way. Start with dry_run:true to preview the verdicts, then re-run with dry_run:false to prune. Confirm the spend with the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoPreview verdicts without deleting (default false; still billed)
thresholdNoDelete posts scoring below this (default 5)
campaign_idYes
review_rulesNoRelevancy rules, e.g. 'Keep only posts where the author has a real problem our product solves'. Falls back to rules saved on the campaign.

TDQS

A4.4/5.0
Behavior5/5

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

The description clearly discloses the destructive behavior and dry-run protection, and adds important non-obvious details not visible in annotations: the 2-credit-per-post cost and the fact that dry runs are billed too. It also warns to confirm spend with the user, which is valuable behavioral context for an agent.

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?

Three sentences, front-loaded with the core action and dry-run behavior, followed by cost and workflow. Every sentence contributes necessary safety or sequencing information; no filler.

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 destructive, paid tool, the description is nearly complete: it covers dry-run, deletion, cost, billing, and safe sequencing. There is no output schema, so a slightly more explicit description of the verdict output format would make it fully complete, but this is enough for safe 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?

The schema already describes dry_run, threshold, and critical billing behavior, so the description adds only marginal value for parameter meaning beyond that. It reinforces the dry-run workflow but does not clarify anterior-to-schema parameter values or add meaning for campaign_id.

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 states a specific verb ('score' and 'delete') and a clear resource ('every discovered post'). It also explains the dry_run case (kept), which makes it easy to tell apart from sibling tools like get_posts or generate_posts.

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 gives an explicit safe workflow: start with dry_run:true, preview verdicts, then re-run with dry_run:false, and confirm spend with the user. It does not name alternatives for hand-off to sibling tools, so it stops short of explicit when-not-to-use 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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TDQS

A4/5.0
Disambiguation4/5

Most tools map cleanly to a distinct resource and action: campaigns, posts, products, research, content spaces, and submissions. A few pairs are close—`create_campaign` vs `create_content_campaign` and `check_balance` vs `get_earnings`—but the detailed descriptions make the intended selection clear.

Naming Consistency4/5

The set largely follows a consistent `verb_noun` snake_case pattern (`list_campaigns`, `generate_posts`, `update_campaign`, `submit_participation`). Minor deviations keep it from a perfect score: `credit_history` lacks a verb, and `create_campaign` reads as generic next to the more specific `create_content_campaign`.

Tool Count3/5

30 tools is high and makes the server feel heavy, but the domain genuinely spans products, credits, campaigns, research, post discovery, content spaces, boosts, and participation rewards. The breadth mostly earns the count, though it sits on the wrong side of the typical 3–15 range.

Completeness4/5

The core end-to-end workflow is covered: create and configure campaigns, discover and refine posts, run research, find opportunities, submit work, and measure results. Gaps exist but are minor for the central purpose—product listings can’t be updated or deleted, and delegates can be added but not removed.

Resources