Show the user's default paper size and orientation preferences (set on their account page). Useful when the user hasn't specified pageSize/orientation explicitly — call this to honor their defaults instead of using A4/Portrait blindly.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -{
- "properties": {
- "default_orientation": {
- "description": "User's preferred page orientation",
- "type": "string"
- },
- "default_page_size": {
- "description": "User's preferred page size",
- "type": "string"
- }
- },
- "required": [
- "default_page_size",
- "default_orientation"
- ],
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful context about preferences being set on the account page and the purpose of honoring defaults, but does not detail edge cases like missing defaults or return format.
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 two sentences, front-loads the core purpose, and includes a practical usage note with no wasted 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?
For a simple zero-parameter read-only tool with rich annotations, the description fully covers what the tool does and when to use it. No output schema means return details are implied by the tool's purpose, which is adequate here.
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?
The tool has zero parameters and schema coverage is 100%, so there is no parameter documentation burden. The description adds clarity about what the tool returns (preferences) without needing param details.
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?
Description clearly states the tool shows the user's default paper size and orientation preferences, using a specific verb and resource. This distinguishes it from sibling tools like convert_document and template listing 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?
Explicitly explains when to use this tool: when the user hasn't specified pageSize/orientation, and provides the alternative of blindly using A4/Portrait. This gives clear context for selection.
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 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.
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.
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.
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.