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This connector has been deprecated

This connector has been replaced by https://glama.ai/mcp/connectors/io.favcrm/favcrm/admin

get_loyalty_balance

Read-onlyIdempotent

Get a member's current loyalty balance — points, stamps, credits, lifetime totals, and active membership tier. Returns null if the member is not enrolled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral context: the tool returns null for non-enrolled members and enumerates the exact components of the balance, going beyond what annotations provide.

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?

The description is a single concise sentence that is front-loaded with the core action and resource. It lists the included data elements efficiently and adds the null-return condition without any fluff.

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

Completeness5/5

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

With an output schema present, the description need not list every return field, but it does disclose the key conditional behavior (returns null if not enrolled) and the high-level contents. For a single-parameter read tool, this is fully complete and actionable.

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

Parameters3/5

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

Schema description coverage is 100%: the single required parameter accountId is described as 'The member/account ID'. The description adds no additional parameter detail, but the schema already fully documents it, so baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool's purpose: 'Get a member's current loyalty balance' and enumerates specific components (points, stamps, credits, lifetime totals, active membership tier). This specific verb+resource combination distinguishes it from siblings like get_membership_tier and get_member_profile.

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

Usage Guidelines3/5

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

The description implies usage context by naming the exact data returned (loyalty balance, tier) and the null condition when the member is not enrolled. However, it does not explicitly state when to use this tool over alternatives like get_membership_tier or list_reward_schemes, nor does it provide exclusion criteria.

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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