cursor-api-mcp

download_ai_code_changes_csv

Export AI code change metrics to CSV. Filter by start date, end date, and user to obtain targeted analytics data.

Instructions

Download AI change metrics CSV (GET /analytics/ai-code/changes.csv).

Returns the CSV body as text under csv_text.

Args: start_date: Optional start date bound. end_date: Optional end date bound. user: Optional single-user filter.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "download_ai_code_changes_csv"
  }
}

Input Schema

Name	Required	Description	Default
`user`	No
`end_date`	No
`start_date`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 29, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are present, so the description must carry the full burden. It discloses the HTTP method (GET) and that the response contains 'csv_text', but does not specify whether the operation is read-only, any side effects, permission requirements, or rate limits.

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?

The description is concise, using two sentences and a bullet-like list for parameters. It avoids unnecessary verbosity while covering the core action and parameters.

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

Completeness3/5

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

For a simple download tool, the description covers the main function and parameters. However, it lacks details on date formats, user identifier format, potential errors, and the full output schema (though output schema exists externally).

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?

The input schema has 0% description coverage, but the description adds brief semantic labels for each parameter (e.g., 'Optional start date bound'). This helps the agent understand parameter purpose beyond what the schema provides (names and types only).

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

Purpose4/5

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

The description clearly states 'Download AI change metrics CSV' specifying the verb and resource. It also includes the HTTP endpoint. However, it does not distinguish from the sibling tool 'download_ai_code_commits_csv', which has a very similar name and purpose.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'list_ai_code_changes' or the commits CSV download. The description only lists parameters, leaving the agent to infer usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server