get_how_to_solveOne «روش حل» page: short answer, when to use the strategy, the common mistake, related station and links. Worked examples and the try-it question are only on the page itself (url). Returns Persian (fa-IR, RTL) text from public fekrestan.com pages for parents and teachers. No answers to in-app exercises.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Page slug from list_how_to_solve |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description offers critical behavioral details beyond annotations, specifically mentioning that the content is in Persian (fa-IR, RTL) and what parts of the page it includes (e.g., worked examples). It also sets clear boundaries by excluding in-app answers.
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 information is dense and packed with value, but it is structured logically by first detailing the content provided and then the technical constraints (language, source, exclusions).
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 the lack of an output schema, the description is extremely complete, detailing not just what the tool fetches, but the specific internal components (strategy, mistakes, links) found on the resulting page.
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 only defines 'slug' as a string, but the description adds semantic value by explaining that the slug is from the specific 'list_how_to_solve' collection. This helps the agent understand the nature of the requested input.
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 defines this tool's purpose as retrieving a structured 'how-to-solve' page, distinguishing it from general article retrieval tools. It specifies the exact educational content returned, like 'short answer' and 'common mistake'.
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 provides excellent context by specifying the target audience (parents and teachers) and the type of content (Persian educational strategy). While it doesn't explicitly name alternatives, its highly specialized nature for 'how-to-solve' pages implies clear usage boundaries.
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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