List Tomorrow Central tools
list_tools_availableList the Tomorrow Central tools this platform offers (id, name, what it does).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
list_tools_availableList the Tomorrow Central tools this platform offers (id, name, what it does).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safe-read nature is known. The description adds valuable context by specifying what the tool returns (id, name, what it does), which goes beyond the bare annotation. No other behavioral aspects need disclosure for such a simple listing operation.
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?
A single, front-loaded sentence with no wasted words. It conveys the action, the resource, and the return contents efficiently. Every word earns its place.
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, parameterless, read-only listing tool with no output schema, the description adequately covers the essentials: what is listed and what fields are included. It could theoretically mention ordering or filtering, but such details are unlikely to be critical for a tool-discovery feature. The annotations and empty schema complete the picture.
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?
There are zero parameters and schema coverage is 100%, so the schema fully documents the absence of inputs. The baseline for 0 params is 4, and the description adds no unnecessary parameter details, which is appropriate.
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 the specific verb 'List' with a clear resource, 'Tomorrow Central tools', and explicitly states the output fields (id, name, what it does). This unambiguously distinguishes it from all sibling tools, none of which have a listing-tools purpose.
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 makes it clear this is for discovering what tools the platform offers. While it doesn't explicitly state 'use when you need to see available tools' or exclude alternatives, there is no alternative tool for this purpose, making the intended usage implicitly obvious.
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.
Each tool targets a distinct resource and action: connection lifecycle (create, verify, get, list), job lifecycle (run, get status, get result), and findings (list). Even get_job_result and list_cost_findings are clearly differentiated as raw vs. analyzed data, and whoami/list_tools_available serve metadata purposes.
Most tools follow a consistent verb_noun pattern (create_, get_, list_, run_, verify_). The only outlier is 'whoami', which breaks the pattern but is a recognizable convention for account identification. Overall naming is predictable and readable.
With 10 tools, the set is well-scoped for a cloud cost scanning platform. Each tool serves a clear purpose in the connection-scan-result workflow, with no redundancy or bloat.
The core scan workflow is covered (connect, verify, scan, get job, get findings), but there are notable gaps: no tool to delete/disconnect a cloud account, and no way to list past jobs or retrieve results without a prior job_id. These missing lifecycle/history operations could force agents to rely on external state or fail when context is lost.