list_facts_to_provideThe universal screening facts this account has not answered yet (right to work, notice period, salary expectation, …), ranked by how many waiting applications each one unblocks. Answer them with save_answer.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already declaring readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the description adds useful behavioral context: it returns unanswered facts for the current account and ranks them by unblocking impact. No contradiction with annotations.
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?
One sentence carries the resource, the scope, the ranking logic, examples, and the intended follow-up action. Nothing is wasted, and the key information is front-loaded.
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?
Given zero parameters, rich annotations, and no output schema, the description is largely complete: it tells the agent what is listed, for which account, in what order, and what to do next. A small gap is that it does not describe the exact output shape needed for save_answer, but the guidance to 'Answer them with save_answer' implies interoperability.
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 and schema coverage is 100%, so the description does not need to explain parameters. The baseline of 4 for zero-parameter tools applies here.
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 states a specific resource ('universal screening facts this account has not answered yet'), a clear verb ('list'), and an ordering principle ('ranked by how many waiting applications each one unblocks'). It also clarifies the relation to the follow-up tool save_answer, so an agent can identify what this tool does.
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 implies when to use the tool: when the agent needs to find which unanswered screening facts would unblock waiting applications. It also names the natural next step ('Answer them with save_answer'), but it does not explicitly contrast with siblings like list_blocking_questions or get_profile_gaps.
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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Tool purposes are generally distinct and well-described, but a few clusters overlap in function: answer_screening_question vs save_answer both write to the answer book, get_my_profile vs get_account both report plan status, and the CV preview/sent-CV/base-CV tools could be confused. The detailed descriptions mitigate most misselection, so this is only a minor issue.
The set almost uniformly uses snake_case verb_noun names like list_, get_, update_, create_, delete_, and start_/stop_. Minor deviations such as login, describe_what_i_want, and the get_my_* vs list_* alternation prevent a perfect score, but the overall pattern is predictable and readable.
49 tools is far above the 25+ threshold and will burden agent tool selection even though many are legitimate single-purpose operations. Several groups could be consolidated—billing links, API-key management, and the CV PDF family—without hurting clarity.
The surface covers the full lifecycle: account creation/auth, profile and CV, targeting, matching, apply runs, screening answers, tracking, billing, export, and deletion. Minor gaps remain, such as no application-level detail/withdrawal endpoint and no direct way to save a parsed CV without re-uploading, but agents can work around them.