optimization_list_decisionsList tenant-owned TikTok optimization recommendations. Decisions contain source snapshot and evidence coverage; they are not proof that any mutation occurred.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| status | No | ||
| connection_context | No |
Changes observed during successful MCP inspections.
Input schema / properties / connection_contextAdded value: +{
+ "pattern": "^mctx_v1_[0-9a-f]{64}$",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this a safe, read-only, closed-world operation, so the safety profile is covered. The description earns credit by adding non-obvious semantics: decisions carry a source snapshot and evidence coverage, and crucially are 'not proof that any mutation occurred' — a meaningful clarification for an agent interpreting results. Pagination, ordering, and default-limit behavior remain undisclosed.
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 tight sentences with the subject and scope front-loaded and no filler. Nothing is wasted, though the second sentence is doing heavier lifting than its brevity suggests.
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?
No output schema exists, and the description does explain what a decision record represents, which helps for a list tool. However, with 0% parameter coverage and no mention of result shape, pagination, or how the status filter narrows results, an agent still lacks enough to invoke it reliably.
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 description coverage is 0% for three parameters (limit, status, connection_context), none of which are described in the schema. The description mentions neither the limit/default-50 semantics, the open/prepared/dismissed status filter, nor the connection context string, so it fails to compensate for the coverage gap.
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 (List) and resource (tenant-owned TikTok optimization recommendations/decisions), and the scope is clear enough to distinguish it from siblings like optimization_dismiss_decision and optimization_prepare_action. Minor muddle: the name says 'decisions' while the description says 'recommendations', leaving the two terms implicitly equated rather than defined.
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?
No statement of when to use this tool versus optimization_evaluate, optimization_prepare_action, or optimization_dismiss_decision, and no prerequisites or exclusions are given. The reading is only inferable from the verb 'List'.
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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