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imachiever

Swiggy AI Insights MCP Server

by imachiever

get_analytics

Retrieve analytics from your Swiggy order history. Analyze spending, timing, restaurants, and cuisines by specifying date ranges and analysis type.

Instructions

Get comprehensive analytics from persistent storage

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateNoEnd date YYYY-MM-DD (optional)
start_dateNoStart date YYYY-MM-DD (optional)
analysis_typeNoAnalysis type: summary, spending, timing, restaurants, cuisinessummary
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Get', implying a read, but does not mention potential side effects, performance implications, required permissions, or what 'persistent storage' implies. The description adds no meaningful behavioral context.

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

Conciseness2/5

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

The one-sentence description is short but contains filler phrases like 'comprehensive' and 'from persistent storage' that add no value. It lacks structured, front-loaded information that would help an agent quickly grasp key details.

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

Completeness2/5

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

The tool has no annotations or output schema, and the description is minimal. It fails to explain how date ranges work, what the returned analytics represent, or how analysis_type changes the result. This simple tool still requires more context for safe and effective use.

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 coverage is 100%, so the baseline is 3. All three parameters (start_date, end_date, analysis_type) are described in the schema, including defaults. The description adds no parameter-specific meaning beyond what the schema already provides.

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

Purpose3/5

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

The description clearly uses the verb 'Get' and identifies 'analytics' as the resource, but 'comprehensive' is vague and does not specify what metrics or analysis types are included. It also does not differentiate the tool from siblings like get_orders or get_restaurants, which also fetch data.

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 choose this tool over alternatives. There is no mention of prerequisites, alternative tools, or exclusions. The agent must infer that this tool is meant for aggregated analytics rather than raw data.

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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