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This connector has been deprecated

This connector has been replaced by https://glama.ai/mcp/connectors/io.favcrm/favcrm/admin

earn_loyalty_points

Credit loyalty points or stamps to a member. Use field="points" for points, "stamps" for stamps. Server records a transaction with the reason as audit context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldYesWhich loyalty unit to credit
amountYesPositive integer amount to credit
reasonNoAudit-trail reason (e.g. "Booking completed", "Manual adjustment")
accountIdYesThe member/account ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesTool result payload — shape varies per tool, see the tool description
summaryYesOne-line human-readable summary of the action
renderTypeYesUI rendering hint for the result

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate the tool is a write operation (readOnlyHint=false). The description adds the behavioral detail that the server records a transaction with the reason as audit context, which goes beyond the schema and annotations. It also clarifies the impact of the field parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the main action, and no redundant information. The field usage and audit note are essential, making it very efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple task, full schema coverage, and existing output schema, the description adequately covers the tool's behavior and key parameter semantics. It lacks explicit alternative-tool guidance but that is captured under usage guidelines; overall it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already describes all four parameters with full coverage. The description adds value by explicitly mapping the field enum values to points/stamps and explaining that the reason is used as audit context, enriching the schema's terse descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Credit loyalty points or stamps to a member.' It also specifies the field parameter values. However, it does not distinguish this tool from the sibling tool 'issue_rewards', so it lacks sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'issue_rewards' or when not to use it. The only usage hint is how to fill the field parameter, which is parameter-level, not tool-selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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