List every AI model available on JarvisClaw — GPT, Claude, Gemini, DeepSeek, and more — with the model IDs you plug straight into the chat tool. Free, and the fastest way to see what you can call.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the tool is 'Free' and lists all models, but does not mention any limitations, response format, or potential pagination. This is adequate for a simple list operation but leaves some unknowns.
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 sentences, front-loaded with the main action, and every word adds value. It efficiently conveys what it does, what it returns, and why it is beneficial.
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 no-parameter list tool with no output schema, the description covers the key points: what is listed, the IDs are usable in chat, and it is free. It does not describe the exact output structure, but this is not critical for such a simple tool.
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 tool has zero parameters, so the description need not add parameter semantics. The baseline for 0 params is 4, and the description appropriately focuses on the output and use case instead.
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 starts with a specific verb+resource ('List every AI model available on JarvisClaw') and includes examples (GPT, Claude, Gemini, DeepSeek) that distinguish it from sibling tools like get_api_detail or search_apis. It clearly states the tool's output (model IDs) and its purpose (for use in the chat tool).
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 clear context for when to use it: to get model IDs for the chat tool, and calls it 'the fastest way to see what you can call.' However, it does not explicitly mention alternatives or when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct resources, but aip_estimate_cost and aip_resolve both provide pre-execution pricing, and chat overlaps with the chat intent inside aip_execute_with_budget. Descriptions clarify the differences reasonably well, but an agent could still pick the wrong one when estimating cost or sending a chat.
Tool names mostly follow an imperative snake_case verb_noun pattern such as list_models, search_apis, and discover_agents, with AIP functions sharing an aip_ prefix. Minor deviations like 'chat' and 'aip_resolve' lacking object nouns are easy to predict and do not create confusion.
With 9 tools, the set covers model listing, chat, AIP routing/execution, API discovery, and agent discovery without bloating. Each major workflow has a focused set of tools, and none feel unnecessary.
The AIP lifecycle is well covered — list intents, resolve, estimate cost, and execute with budget — and chat has list_models + chat. However, as an API Marketplace there is no direct call_api or invoke tool, and no publish/management surface, so search_apis and get_api_detail lead to an external action rather than completing the loop in-server.