Get the full spec for any API returned by search_apis — pricing, method, and the exact request to send. Works for both a user-published API slug and a catalogue handle like 'federation/40'. Use this before wiring a call in.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | A user-published API slug, or a catalogue handle ('federation/40' or just '40'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It adds valuable context by specifying the output contents (pricing, method, exact request) and the input flexibility (slug or catalogue handle, including shorthand '40'). This goes beyond the bare name and helps the agent understand what to expect.
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 main purpose and followed by input format details. Every word earns its place with no redundancy or filler.
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 single-parameter tool with no output schema and no annotations, the description is nearly complete: it covers what the tool does, what the input can look like, and when to use it. It doesn't address error conditions or prerequisites, but for a read-only lookup this is acceptable.
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 provides 100% parameter coverage, explaining both slug formats. The description restates this information but adds the key context that the slug comes from search_apis results. This is helpful but doesn't significantly extend beyond the schema's description.
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 the tool's function: retrieving the full spec for an API, including pricing, method, and exact request. It distinguishes itself by referencing its input source (search_apis) and its position in the workflow (before wiring a call), making its purpose 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 explicitly tells when to use the tool ('Use this before wiring a call in') and clarifies accepted input formats (user-published slug or catalogue handle). It doesn't name alternatives or exclusions, but the context is strong enough to guide an agent on appropriate usage.
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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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.