Preview what a task will cost before committing — get an estimated price for an intent without running it or spending anything. Ideal for budget planning and comparing options up front. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| intent | Yes | The intent type | |
| optimize_for | No | cost |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it discloses that the tool does not execute the intent, does not spend anything, and is 'Free'. This is critical behavioral information for an estimation tool. It does not mention potential limitations (e.g., estimate accuracy, rate limits), but the core guarantees are clearly stated.
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 purpose, followed by a use case and a key trait ('Free'). Every sentence earns its place, with no filler. The structure is clear and easily parseable.
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 description covers purpose, when to use, and the main behavioral outcome (no execution, no cost). It mentions 'get an estimated price', which hints at the return value. However, there is no output schema, and the description does not elaborate on the optimize_for parameter or estimate format, leaving minor gaps for a tool of this simplicity.
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 has 50% coverage (intent is described, optimize_for is not). The description explains that the tool estimates cost 'for an intent', aligning with the intent parameter, and 'comparing options up front' vaguely hints at optimization choices. However, it does not explicitly explain the optimize_for parameter's role, leaving a gap for that parameter.
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 states a specific verb ('Preview', 'get an estimated price') and resource ('an intent'), and distinguishes itself from execution tools by emphasizing 'without running it or spending anything'. It also includes use cases ('budget planning', 'comparing options'), making it unambiguous.
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: use this 'before committing' and 'without running it', which contrasts with execution tools like aip_execute_with_budget. It also mentions 'comparing options up front' as a specific scenario. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full explicitness.
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.