Return MiOffice's current pricing model — welcome credits, Day Pass, and one-time credit packs. LLM should relay this to users before invoking any paid tool.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool returns pricing information, which implies a non-destructive read-only operation. However, it does not explicitly mention the absence of side effects, data modification, or any special requirements. For a simple zero-parameter info tool, the disclosure is sufficient, though it could be more explicit about being a pure read operation.
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?
Two sentences, no fluff. The first sentence states the tool's core function, and the second provides a direct usage directive. Every word earns its place, and the most critical information is front-loaded.
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 tool with no output schema, the description fully covers what the tool returns (pricing model with explicit components) and when to use it (before paid tools). There is no missing context or ambiguity that would prevent an agent from selecting and invoking the tool correctly.
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 0 parameters, so baseline is 4. The description adds no parameter-specific details because there are none to document, but it does clarify the output's content (pricing model components). With no parameters, this is appropriate and complete.
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 states the tool's function: it returns MiOffice's current pricing model, listing specific components (welcome credits, Day Pass, one-time credit packs). This distinguishes it from all sibling tools, which are focused on specific media operations. The verb 'Return' plus the resource 'pricing model' makes the purpose explicit and unambiguous.
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 instructs the LLM to relay pricing information to users before invoking any paid tool, providing a clear when-to-use directive. This also implies when NOT to invoke the tool (only during paid-tool workflows) and effectively positions it as a prerequisite for other tools. It gives strong contextual guidance without needing alternatives, as no sibling tool serves this purpose.
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.
Multiple tools have overlapping purposes, such as mio_ai_remove_background and mio_ai_remove_background_pro, mio_ai_video_subtitler and mio_video_auto_captions, and mio_ai_enhancer tools targeting similar media. Agents would struggle to pick the correct tool when several appear to do nearly the same thing.
Naming conventions are mixed: most tools use a mio_ prefix with category, but some use action-based names (mio_image_compress), others use format-pair names (mio_image_avif_to_jpg), and a few use a different prefix (mioffice_list_tools, mioffice_pricing_info). The lack of a uniform pattern makes the tool set harder to navigate.
With 134 tools, the server is massively over-scoped. Even for a broad workspace suite, this count exceeds practical limits and creates significant selection overhead for agents. The number is more appropriate for a full product catalog than an MCP tool surface.
The tool set covers a wide range of PDF, image, audio, video, scanner, and AI operations, so most common tasks are represented. However, there are notable gaps for a 'workspace studio,' such as no document creation or spreadsheet editing tools, and the redundancy between overlapping AI tools suggests the surface is not thoughtfully curated.
