Forget a saved answer
delete_saved_answerRemove one answer from the answer book (an answer_id from list_saved_answers).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| answer_id | Yes |
delete_saved_answerRemove one answer from the answer book (an answer_id from list_saved_answers).
| Name | Required | Description | Default |
|---|---|---|---|
| answer_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=false, so the description doesn't need to restate that this is a destructive action. It adds a small amount of behavioral context by requiring the answer_id to come from list_saved_answers, but it doesn't describe what happens if the ID is invalid or already deleted.
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 sentence delivers the action, the object, and the source of the parameter without any fluff. The parenthetical is efficient and front-loaded information placement is excellent.
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 one-parameter delete tool with annotations covering destructiveness and idempotency, the description is largely complete. It references the correct sibling for obtaining IDs and gives a clear mental model of the 'answer book.' Lack of output-schema or error-handling detail is a minor gap for such a simple operation.
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?
With 0% schema description coverage, the description compensates by explaining that answer_id identifies an existing answer from list_saved_answers. This adds meaning beyond the bare string type in the schema, though it could go further by specifying format or validation behavior.
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 'Remove' and identifies the resource as 'one answer from the answer book,' making the tool's function unambiguous. The parenthetical reference to list_saved_answers also helps distinguish it from save_answer and other answer-related tools.
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?
It clearly implies the correct workflow: call list_saved_answers to obtain a valid answer_id, then pass it here. It doesn't explicitly say when not to use this tool or name alternatives, but the intended usage context is clear enough for a simple delete operation.
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.
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.