dk_suggestConvert vague or misspelled product queries into keywords, categories, and brands to guide Digikala search or category browsing.
Autocomplete a vague or misspelled query into keywords, categories and brands.
Next: dk_search with a keyword, or dk_category_products with a category code.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Partial text, e.g. 'samsung' or 'هدفون'. |
Changes observed during successful MCP inspections.
v0.4.0Input schema / properties / query / titleRemoved value: -"Query"Input schema / titleRemoved value: -"dk_suggestArguments"Output schema / (root)Previous value: -{
- "additionalProperties": true,
- "title": "dk_suggestDictOutput",
- "type": "object"
-}New value: +nullv0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds value beyond that by disclosing the return shape (keywords, categories, brands) in a tool with no output schema, though it says nothing about result limits or ranking.
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 compact lines: the capability first, the follow-on routing second. No filler, and everything 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 one-parameter, read-only lookup with no output schema, the description covers the input expectation, the returned entity types and the next step, which is everything an agent needs to call it and chain it 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?
Schema coverage is 100% and the single parameter already has an example, so the baseline is 3. The description adds semantic meaning by indicating the query is expected to be partial/vague or misspelled, which frames how to populate it.
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?
States a specific verb and resource ('Autocomplete a vague or misspelled query') plus the nature of the output (keywords, categories, brands). It also names the sibling tools it feeds into, so an agent can place it precisely in the workflow.
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 condition for use is explicit ('vague or misspelled query'), and the two downstream alternatives (dk_search, dk_category_products) are named with the input each expects. It stops short of stating when NOT to call this (e.g. skip it if you already have a clean keyword), so it is clear context rather than full routing logic.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.