get_balanceReturn total, reserved, and available MainBook credits. Every value is measured in PDF pages.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| units | No | pages | |
| balance | Yes | ||
| reserved | Yes | ||
| available | Yes | ||
| explanation | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and open-world behavior. The description adds value by specifying the unit (PDF pages) and the three credit components, going beyond annotations. No contradictions with 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 consists of two short, front-loaded sentences that convey everything needed without extraneous words. Every sentence adds value.
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 there are no parameters and an output schema exists, the description fully and succinctly explains the tool's purpose and output semantics. No missing information for this simple tool.
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?
There are zero parameters, so the description carries no burden to explain parameters. The schema coverage is 100% (trivially), and the description clarifies the return meaning without needing to elaborate on inputs.
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 identifies the tool as returning three specific credit values (total, reserved, available) measured in PDF pages. It distinguishes itself from sibling tools by focusing solely on balance queries, while siblings handle conversions and listings.
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 implicitly clarifies when to use this tool (to check credit balance) but does not explicitly state when not to use it or provide alternatives among siblings. Given the simple, unique purpose, explicit exclusions are unnecessary.
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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Each tool has a clearly distinct purpose: starting a conversion, checking credits, retrieving a specific conversion, and listing conversions. There is no overlap or ambiguity among the four tools.
All tool names follow a consistent verb_noun pattern: convert_bank_statement, get_balance, get_conversion, list_conversions. This makes the naming predictable and easy to reason about.
With exactly 4 tools, the set is well-scoped for a bank statement converter. Each tool covers an essential part of the workflow without unnecessary redundancy or bloat.
The tool surface covers the full lifecycle: initiating a conversion, checking credit balance, retrieving results, and listing past jobs. There are no significant gaps for the stated purpose of converting and retrieving bank statements.