This connector has been replaced by https://glama.ai/mcp/connectors/io.favcrm/favcrm/admin
List customer segments. Segments group members by criteria for targeted campaigns.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 20) | |
| search | No | Search by segment name |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Tool result payload — shape varies per tool, see the tool description | |
| summary | Yes | One-line human-readable summary of the action | |
| renderType | Yes | UI rendering hint for the result |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds minimal behavioral context beyond defining segments. It doesn't discuss pagination, performance, or other traits needed beyond 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 two sentences with no extraneous information. It is front-loaded with the verb and resource, making it efficient and easy to parse.
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 list tool with a clear name, annotations, and an output schema, the description provides sufficient context about what segments are. It could mention that it returns a list, but the output schema covers that.
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 coverage is 100% with descriptions for both parameters. The description does not add further meaning or context about the parameters, so it meets the baseline expectation.
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 customer segments and explains what segments are. The name 'list_segments' is self-explanatory, and the description distinguishes it from siblings like 'get_segment' by indicating it's a list operation.
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 does not explicitly state when to use this tool over alternatives like 'get_segment'. There is no guidance on context or exclusion, leaving the agent to infer usage from the tool name alone.
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.
Each tool targets a distinct resource and action, with clear descriptions that minimize ambiguity. Even related tools like create_post vs create_post_type are well-separated by their targets.
All tools follow a consistent verb_noun pattern (e.g., create_account, list_services, update_post), with no mixing of naming conventions. The pattern is predictable throughout the set.
190 tools is excessively large for any server, far exceeding the typical 3-15 tool range. The sheer volume overwhelms agents and suggests poor scoping, even for a comprehensive CRM platform.
The tool set covers CRUD operations across many domains (CRM, bookings, marketing, CMS, etc.), but notable gaps exist (e.g., no delete_account, delete_contact, update_booking). These are minor given the vast surface.