muse_normalize_for_platform

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

With no annotations provided, the description carries full responsibility for transparency. It discloses that the tool handles various markdown elements, but it does not mention side effects (e.g., whether the original document is modified), return type (likely a string), or error conditions. The description is moderately transparent but could be improved.

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 a single, well-structured sentence that front-loads the main action and key details. Every word adds value; there is no redundancy or extraneous information.

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?

The description covers the tool's purpose and supported elements but lacks information about the output format (e.g., returns a string) and does not discuss error handling or limits. For a tool with nested options and no output schema, the description should provide more context about the result.

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 schema describes all parameters and sub-properties, so the description adds limited new meaning. However, it reinforces that the platform parameter targets specific platforms and hints at the scope of options. Since schema coverage is high, a baseline score of 3 is appropriate.

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 purpose: converting Markdown documents for specific platforms (Notion, GitHub Wiki, Obsidian) and lists the elements handled. It distinguishes itself from sibling tools like muse_analyze_code or muse_publish_document by focusing on normalization for target platforms.

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 lacks guidance on when to use this tool versus alternatives. For example, it does not mention that muse_publish_document might be more suitable for full publication workflows, or provide any exclusion criteria. The usage context is only implied by the platform enumeration.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.