list_listsList contact lists. Returns list IDs, names, subscriber counts, and creation dates.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 20, max 100). | |
| _apiKey | Yes | ActiveCampaign API key (UI: Settings → Developer). | |
| account | Yes | Your ActiveCampaign account URL or subdomain, e.g. "https://youraccount.api-us1.com" or just "youraccount". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-activecampaign-api-key",
+ "account": "myaccount",
+ "limit": 100
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, so the tool is safe and non-destructive. The description adds concrete output fields (list IDs, names, subscriber counts, creation dates), which helps agents understand what data to expect. No contradictions with annotations.
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 a single, front-loaded sentence that immediately communicates the action ('List contact lists') followed by specific return details. No extraneous words or repetition.
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 nested objects), the description covers purpose and return structure adequately. However, it lacks details on pagination or result ordering, which would improve completeness for an agent.
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 100%, with each parameter (limit, _apiKey, account) fully explained. The tool description does not add additional semantics beyond restating output fields. Baseline 3 is appropriate.
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 contact lists and specifies the returned fields (IDs, names, subscriber counts, creation dates). This distinguishes it from sibling tools like list_contacts (which lists contacts) and get_contact (single contact). The verb and resource are explicitly defined.
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?
No explicit guidance on when to use this tool versus alternatives. The description implicitly suggests it is for retrieving an overview of all lists, but does not mention when to use it over specific list retrieval tools (if any existed) or any prerequisites. Sibling tools are not addressed.
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.
Several tools occupy nearly the same role: ask_pipeworx and ask_pipeworx_beta are described as behaviorally identical, ask_pipeworx_grounded and deep_research both route questions across the same large catalog, and the Polymarket tools overlap heavily in intent. The ActiveCampaign list/get tools are distinct, but an agent facing 37 tools would frequently struggle to choose the right research or betting tool.
Some clusters are internally consistent (list_*, ask_pipeworx_*, polymarket_*), but the server overall mixes bare verbs like remember and forget, noun phrases like entity_profile and deep_research, and brand-prefixed names like pipeworx_trending and polymarket_arbitrage. There is no unified naming convention across the tool set.
37 tools is excessive for an ActiveCampaign integration, and only 6 of them actually relate to ActiveCampaign. The rest are a broad Pipeworx data, research, and prediction-market utility suite, so the count is not well-scoped to the server's stated purpose.
As an ActiveCampaign server, the surface is severely incomplete: it only provides read-only list/get operations and no create, update, delete, send, tag, or workflow-management tools. The unrelated Pipeworx tools may be feature-rich, but they do not fill the gaps in the named ActiveCampaign domain.