get_applicationReturn one of the caller's applications: its job, documents status, auto-apply state, when it was sent, and how many AI edits are left. Read the resume or cover letter itself with get_document.
| Name | Required | Description | Default |
|---|---|---|---|
| application_id | Yes | An application id from list_applications. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations successfully handle the behavioral safety profile (readOnlyHint: true), allowing the description to focus on content scope (e.g., AI edit counts), providing moderate, non-redundant value.
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 highly efficient. The first sentence immediately defines the output scope, and the second sentence provides essential cross-tool operational guidance in the most concise manner possible.
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?
Even without an output schema, the description is highly complete as it defines the structure of the returned application object (job, status, edit counts). The instructions regarding `get_document` cover critical usage context.
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?
Schema coverage is 100%, meaning the schema fully documents the `application_id`. Therefore, the description provides no unique semantic value beyond what the structured input schema already specifies.
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 (returning one application's metadata) and enumerates specific fields returned, such as AI edits left and documents status.
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 provides an explicit guideline by instructing the agent to use the sibling tool `get_document` when the user needs to read the document content itself, effectively defining the tool's boundaries.
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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