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Manage Saved Searches

aiapplyd_manage_saved_searches
Destructive

Manage the user's saved searches: named snapshots of their job preferences, with optional email alerts for new matches. action "list" shows them; "save" saves the current job preferences as a new search, with an optional name and alerts_enabled; "update" renames one or turns its email alerts on or off; "delete" removes one for good, so call it only when the user asks. Nothing is sent to any employer and nothing is spent. Do not use it to change what the user is matched with; use aiapplyd_update_job_preferences. Next: aiapplyd_get_matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoFor save and update: the name of the search. When save gets none, it names the search after the first role and location.
actionYeslist: every saved search. save: save the current job preferences as a new search. update: rename one or turn its email alerts on or off. delete: remove one for good.
alerts_enabledNoFor save and update: true emails the user new matches for this search, false stops the emails.
saved_search_idNoThe saved search, from action list. Required for update and delete.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
savedYesaction save or update: the search after the change.
actionYes
searchesYesaction list: every saved search.
deletedIdYesaction delete: the search that was deleted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare destructiveHint=true, readOnlyHint=false, and non-idempotency, so the safety profile is covered. The description adds real context beyond that: 'delete removes one for good' flags irreversibility, and 'Nothing is sent to any employer and nothing is spent' bounds side effects. It does not describe alert delivery behavior or timing, keeping it just short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense paragraph that front-loads what the resource is, then walks the actions in order, then handles routing and safety. Every clause carries new information with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values need not be described, and the description covers action semantics, the prerequisite that saved_search_id comes from action list, and the destructive nature of delete. An agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters (name, action, alerts_enabled, saved_search_id) are already documented in the schema, including the default naming behavior when save receives no name. The description restates the action semantics and which params apply to which action but adds no syntax or format detail beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Manage the user's saved searches: named snapshots of their job preferences') and enumerates each action's effect. It distinguishes itself from siblings by explicitly naming aiapplyd_update_job_preferences as the tool for changing match preferences and pointing to aiapplyd_get_matches as the next step.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives per-action when-to-use guidance ('action "list" shows them', 'call it only when the user asks' for delete) plus an explicit exclusion and the alternative to use instead ('Do not use it to change what the user is matched with; use aiapplyd_update_job_preferences'). Nothing is left to inference.

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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