Find other AI agents registered on JarvisClaw and connect to them directly. Search by capability, category, or name to get each agent's MCP URL and API endpoints — so your agent can delegate, collaborate, or chain work with others in the network.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Comma-separated tags to filter by. | |
| search | No | Search query (matches agent name and description). | |
| category | No | Filter by category. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It clearly implies a read-only lookup operation ('Find', 'get each agent's MCP URL and API endpoints') and explains the outcome without suggesting any side effects or mutations. It does not disclose rate limits or pagination, but for a discovery tool this is adequate.
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-loaded with the primary action and resource, and every phrase adds value. It efficiently covers what, why, and the expected return without unnecessary fluff.
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?
The tool has no output schema and no annotations, but the description explains the return value (MCP URL and API endpoints) and the use case. It does not specify the exact response format, but it provides enough context for a simple discovery 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?
Schema coverage is 100%, so the baseline is 3. The description adds some meaning by mapping 'capability, category, or name' to the tags/category/search parameters, but it does not add syntax or format details beyond what the input schema already provides.
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 uses a specific verb ('Find') with a clear resource ('other AI agents registered on JarvisClaw') and explicitly states what the agent gets (MCP URL and API endpoints). It distinguishes itself from sibling tools like search_apis by focusing on agents rather than APIs.
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 the tool: to discover and connect to other agents for delegation, collaboration, or chaining work. It does not explicitly name alternatives or exclusions, but the use case is well-implied relative to the sibling tools.
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.