list_campaignsList ALL the connected user's discovery/boost campaigns, newest first — including ones created in the ProductClank web app, not just via this connector. Free. Use to find a campaign id before get_campaign / generate_posts / get_posts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 20 | |
| offset | No | ||
| status | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, and the description adds useful behavioral context: results are newest first, the list includes campaigns created outside this connector, and the tool is free to use. There is no contradiction with the readOnlyHint annotation. It does not mention pagination, but that is a minor gap for a read-only listing tool.
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 compact and efficient: a single clear sentence defines scope and ordering, followed by a short workflow note and a one-word cost indicator. There is no redundant or filler content.
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?
For a simple read-only listing tool with no required parameters and no output schema, the description provides enough context to use it correctly: what it lists, in what order, and why. It could clarify how limit, offset, and status combine or what the response looks like, but those are inferable and largely covered by the schema's enum and default values.
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?
Schema description coverage is only 33%, and the description does not explain limit, offset, or status. It implies unfiltered 'ALL' behavior and ordering, but leaves parameter semantics to the schema. Since coverage is low, the description needed to compensate and did not.
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 lists ALL of the connected user's discovery/boost campaigns, names the ordering (newest first), and emphasizes its comprehensive scope across both the connector and ProductClank web app. It also places the tool in a workflow by saying it is used to find a campaign id before get_campaign / generate_posts / get_posts, which distinguishes it from those siblings.
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 explicitly gives a concrete use case: find a campaign id before calling get_campaign, generate_posts, or get_posts. It does not explicitly state when to avoid this tool in favor of other list-like siblings such as find_open_campaigns, but the 'ALL' scope and stated workflow make the primary usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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`.
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.
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.