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S2thend

cursor-history-mcp

cursor_history_year_pack

Generate a year-in-review data package from your Cursor AI chat history, producing a sanitized JSON summary with statistics, topics, and keywords, plus a prompt template for LLM-based report generation.

Instructions

Generate a year-in-review data package from Cursor AI chat history. Produces a sanitized JSON summary with statistics, topics, and keywords, plus a prompt template for LLM-based report generation. Read-only operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoCalendar year to analyze (default: current year)
languageNoReport language preference (default: en)
workspaceNoOptional workspace path to filter sessions
maxSamplesNoMaximum sample questions to include (default: 30)
maxSampleLengthNoMaximum characters per sample (default: 120)
topicsCountNoNumber of topics to extract (default: 7)
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explicitly calls out 'Read-only operation' and 'sanitized JSON', which are key behavioral traits. However, it does not mention performance implications or potential side effects like long processing time.

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?

The description is two sentences, extremely concise, and front-loaded with the core purpose. Every word adds value.

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

Completeness4/5

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

Given 6 optional parameters and no output schema, the description adequately covers the core functionality and safety. It mentions the output format (JSON summary + prompt template). Could be improved by explaining 'sanitized' or typical processing time.

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 baseline is 3. The description does not add any additional meaning beyond what the schema already provides for each parameter. It only gives high-level output summary.

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 clearly states the verb 'generate', the resource 'year-in-review data package from Cursor AI chat history', and the output (sanitized JSON summary plus prompt template). It distinguishes from sibling tools like 'search' or 'list' by being specifically for annual analysis.

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

Usage Guidelines3/5

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

The description mentions it's read-only, implying safety, but does not explicitly state when to use this tool versus siblings like cursor_history_search or cursor_history_show. No alternatives or exclusions are provided.

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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