job-application-tracker-mcp

Import applications

import_applications

Bulk-add job applications from CSV text with required company and role columns, optional status, deadline, and notes; invalid rows are skipped and reported by row number.

Instructions

Add many applications at once from CSV text, for example a list you kept in a spreadsheet. Inputs: csv, the text of the file with a header row. The columns company and role are required; type, status, deadline, interview_date, source and notes are optional, and other columns such as id are ignored. The CSV from export_report works as is. Each valid row is saved as a new application with a new id. A row with a blank company or role, an unknown type or status, or a bad date is skipped and listed with its row number. A row that matches an existing application by company, role and deadline is still saved and gets a warning. Example prompt: "Import these applications from my spreadsheet: company,role,deadline ..."

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "csv": "<csv>"
    },
    "name": "import_applications"
  }
}

Input Schema

Name	Required	Description	Default
`csv`	Yes	The CSV text, with a header row that has at least company and role.

Output Schema

Name	Required	Description
`skipped`	Yes	Rows that were not saved, with the row number in the file (the header is row 1) and the reason.
`imported`	Yes	How many applications were saved.
`warnings`	Yes	Saved rows that may duplicate an existing application.
`applications`	Yes	The applications that were saved.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.1.1Nov 3, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the annotations by disclosing per-row validation behavior: blank company/role, unknown type or status, and bad dates cause the row to be skipped and reported with its row number, and duplicates by company/role/deadline are still saved but warned. It also confirms each valid row gets a new id. This is exactly the partial-success semantics an agent needs before firing a bulk write.

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

Conciseness4/5

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

Front-loads the purpose, then layers columns, then failure handling, then a concrete example prompt — a logical order with little waste. It runs slightly long and the trailing example prompt plus 'for example a list you kept in a spreadsheet' partially duplicate the same idea, keeping it short of a 5.

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?

The tool has one parameter, an output schema that covers return values, and annotations covering the safety profile; the description fills the remaining gaps — column contract, validation failures, and duplicate handling. Nothing an agent needs for a correct call is missing.

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

Parameters4/5

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

Schema coverage is already 100% for the single csv parameter, but the description adds substantial meaning beyond it: the required columns (company, role), the optional columns (type, status, deadline, interview_date, source, notes), and that unknown columns such as id are ignored. That column-level contract is not in the schema and materially reduces malformed calls.

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?

States a specific verb and resource (add many applications at once) plus the input medium (CSV text), which cleanly separates it from the sibling add_application that handles a single record. The spreadsheet origin example makes the bulk intent unambiguous.

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

Usage Guidelines4/5

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

Gives clear context for when to use it (bulk import from a spreadsheet) and explicitly notes that CSV from export_report works as-is, which is valuable cross-tool routing. It never states the inverse case (use add_application for one record), so the alternative routing is implied rather than spelled out.

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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