get_researchRead the cached topic/competitor analysis for a campaign (from run_research). Free. Also reports which of its findings the campaign is actually searching: expanded keywords and exclusion terms always apply, while phrases / influencer accounts / lists / competitors stay dormant until their source is enabled via update_campaign.
| Name | Required | Description | Default |
|---|---|---|---|
| campaign_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds meaningful behavioral details: it is free of charge, and it reports which findings are actually applied (expanded keywords/exclusion terms always active, others dormant until enabled). This provides operational nuance that annotations alone do not convey.
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 two compact sentences with no filler. The main purpose is front-loaded, and additional behavioral nuances are efficiently appended. Every sentence contributes valuable information.
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?
The tool is simple (one parameter, no output schema), but the description fully explains what it returns (cached analysis), how the findings are interpreted (active vs dormant), and how to influence them (via update_campaign). It also notes the free cost, covering all needed context for an agent to decide and call it.
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?
With 0% schema description coverage, the description carries the burden for the 'campaign_id' parameter. It mentions 'for a campaign' but does not elaborate on the ID's format, source, or relationship to other campaign tools. This is minimal compensation, though acceptable for a simple identifier.
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 reads cached research ('Read the cached topic/competitor analysis for a campaign'), specifying the resource and linking it to a specific source ('from run_research'). It distinguishes itself from sibling tools like get_campaign and run_research by narrowing the scope to cached analysis results.
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 implies it should be used after run_research has been called, and explicitly mentions update_campaign as the way to enable dormant findings. However, it does not explicitly name alternatives or state when not to use this tool, leaving some inference to the agent.
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.