list_free_toolsFree AI tools made by other people, collected by us: engines to run models locally, agent frameworks, image and video models, vector databases. We do not sell these, we earn nothing from them, and we have not benchmarked them. Read-only. Nothing here is ours and nothing here is for sale.
| Name | Required | Description | Default |
|---|---|---|---|
| about | No | e.g. video, rag, agents, local |
Changes observed during successful MCP inspections.
Input schema / properties / aboutAdded value: +{
+ "description": "e.g. video, rag, agents, local",
+ "type": "string"
+}Input schema / properties / despreRemoved value: -{
- "description": "e.g. video, rag, agents, local",
- "type": "string"
-}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full behavioral burden. It explicitly states the tool is read-only, that the tools are not owned, not sold, and not benchmarked — useful trust and safety context. It does not describe pagination or response shape, but this is a simple listing tool.
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 short and front-loaded with core purpose, but it contains repetition: 'we do not sell these, we earn nothing from them' is restated by 'nothing here is for sale,' and 'collected by us' overlaps with 'nothing here is ours.' It could be tighter.
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 read-only listing with one optional parameter, the description covers purpose, content scope, and safety. However, it does not explain how the 'about' parameter filters results, nor does it provide guidance relative to sibling tools. That leaves moderate gaps for an agent deciding how to invoke it.
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 already covers the single 'about' parameter with examples, but the description adds richer context by enumerating the kinds of content returned: local model engines, agent frameworks, image/video models, and vector databases. This helps an agent infer meaningful query values.
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 identifies the resource as free AI tools made by others and the action as a listing/collection, with concrete category examples. The qualifiers 'not ours' and 'not for sale' distinguish it from product-selling or comparison siblings.
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 for discovering free third-party tools but gives no explicit guidance on when to use this tool versus siblings like search_shelf, find_by_need, or try_a_model. It does not mention alternatives or exclusion conditions.
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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