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Glama

mdmagic-mcp-server

by io.github.MDMagic-MCP

check_credit_balance

Read-onlyIdempotent

Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status.

CALL THIS PROACTIVELY when:

  • The user asks 'how many credits do I have' or similar

  • After a conversion, if the user wants to know what's left (also returned by convert_document directly)

  • Before a conversion of an unusually large document, to warn the user if balance is borderline

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "purchased_credits": {
-      "description": "Permanent purchased credits",
-      "type": "integer"
-    },
-    "subscription_credits": {
-      "description": "Renewable monthly subscription credits",
-      "type": "integer"
-    },
-    "total_credits": {
-      "description": "Total credits available (subscription + purchased)",
-      "type": "integer"
-    }
-  },
-  "required": [
-    "total_credits"
-  ],
-  "type": "object"
-}New value: +null
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds value by specifying the exact response contents (credit breakdown, plan name, status) and indicating that proactive calls are encouraged. It does not contradict annotations, and while it doesn't mention rate limits or auth, the simple read-only nature plus annotation coverage justify a score above 3.

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 concise and well-structured: a single sentence states the core functionality, followed by a bulleted list of when to call it. Every sentence provides actionable guidance, with no filler or redundancy.

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?

For a no-parameter, read-only tool with no output schema, the description fully covers what the agent needs to know: what data is returned and when to use it. It includes proactive use cases and warns about borderline balances, making it complete for its simplicity.

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?

The tool has zero parameters, so schema coverage is effectively 100%. The description doesn't need to explain parameters, and the baseline for 0-parameter tools is 4. It uses that space to clarify the returned information instead, which 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 function: checking the user's MDMagic credit balance, including subscription credits, purchased credits, plan name, and status. It distinguishes itself from sibling tools (e.g., convert_document, estimate_conversion_cost) by focusing on balance retrieval, making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit proactive usage scenarios: when the user asks about credits, after a conversion, or before processing a large document. It also notes an alternative (convert_document returns the balance directly), giving the agent clear guidance on when to invoke this tool versus relying on other tools.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: credit balance vs cost estimation, template listing scoped to all/built-in/custom, and conversion vs validation. The three list tools are explicitly named by scope, eliminating ambiguity.

Naming Consistency5/5

All tool names follow the same verb_noun snake_case pattern (check_, convert_, estimate_, get_, list_, recommend_, show_, validate_), making the API predictable and easy to navigate.

Tool Count5/5

10 tools is well within the ideal 3-15 range for a document conversion service. Each tool addresses a necessary step in the workflow (template selection, validation, cost estimation, conversion, credit monitoring) without redundancy.

Completeness4/5

The core lifecycle (pre-flight validation, cost estimation, conversion, balance checking, template discovery) is fully covered. Minor gaps exist—no template upload or settings update—but these are likely handled outside the MCP server, so the surface is complete for its intended agent workflows.

Resources