List all available AI content generation tools (caption writers, hashtag generators, content ideas, etc.) with their names, descriptions, and input fields. Optionally filter by keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool lists tools with names, descriptions, and input fields, and that it can filter by keyword. However, it does not mention whether the list is paginated, whether it includes disabled tools, whether it triggers any side effects, or what the exact response shape is.
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 two sentences, front-loads the main purpose, and uses parenthetical examples to clarify scope. It is concise and readable, though the second sentence could be more specific about the filter behavior.
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 list tool with one optional parameter and no output schema, the description is mostly adequate. However, the lack of any behavioral details (pagination, ordering, whether the list is exhaustive) and the absence of explicit guidance on when to use this vs. use_ai_tool leaves some gaps for an agent.
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%, so the description must compensate. It does explain the 'query' parameter's purpose ('Optionally filter by keyword'), which adds meaning beyond the bare schema. However, it does not specify the expected format, case sensitivity, or matching behavior, leaving some ambiguity.
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 states a specific verb ('List') and resource ('AI content generation tools'), and enumerates examples (caption writers, hashtag generators, content ideas). It is clear what the tool does, though it doesn't explicitly differentiate from siblings like use_ai_tool or get_post_options.
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 implies usage: call this to list available AI tools, optionally filtering by keyword. It does not explicitly state when to use this versus use_ai_tool or other listing tools, but the context of 'AI content generation tools' provides reasonable guidance.
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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