This connector has been replaced by https://glama.ai/mcp/connectors/io.favcrm/favcrm/admin
List loyalty reward schemes (points/stamps programs) configured for this business.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 20) | |
| search | No | Search by 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 declare readOnlyHint, idempotentHint, and destructiveHint. The description adds minimal behavioral context beyond stating the scope (for this business), so no contradiction but no significant added value.
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 sentence that is concise, front-loaded with the action and resource, and contains no unnecessary words.
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 simple list operation with an output schema and fully described parameters, the description is complete enough. However, it could briefly mention pagination or default limit to be more explicit.
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%, so the schema already documents both parameters. The description does not add additional meaning beyond what the schema provides.
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 loyalty reward schemes (points/stamps programs) for the business. It uses a specific verb 'List' and resource, distinguishing it from sibling list tools.
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 usage context ('configured for this business') but does not provide explicit when-to-use or when-not-to-use guidance compared to alternatives.
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.