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Suara

by com.trysuara
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Restaurant Index

restaurant_index
Read-onlyIdempotent

Use this when someone asks how restaurants and cafes use Instagram in Kuala Lumpur, Singapore, Jakarta, Bangkok, Sydney, Melbourne, Vancouver, Toronto, London, Dubai: how often they post, likes per 1,000 followers, how much is video or carousels, caption length, hashtags and the busiest posting hours. Returns the Suara Restaurant Index (October 2026, real public accounts, with 90% intervals), for one city compared with all 10 cities, or every city's headline numbers. It measures likes only and shows correlation, not cause: say so, and do not use it to promise results.

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": "restaurant_index"
  }
}

Input Schema

NameRequiredDescriptionDefault
cityNoOne of the 10 cities, optional (leave out for all 10)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses data provenance (October 2026, real public accounts), statistical treatment (90% intervals), output modes (single city vs. all 10 vs. every city's headline numbers), and a critical limitation (likes only, correlation not causation, with an instruction to say so). That is exactly the kind of context annotations cannot carry.

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-loaded with the trigger and organized as trigger, output description, and caveat. The city list and metric list make the first sentence dense, but every element carries decision-relevant information rather than filler.

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?

With no output schema, the description takes on the burden of explaining returns, and it does: index name, vintage, confidence intervals, and the two comparison modes. The statistical caveat is included, so an agent has everything needed to call and present results correctly.

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?

The single city parameter is fully documented by the schema, so the baseline is 3. The description adds meaning the schema doesn't: omitting city yields every city's headline numbers, and a single city is returned in comparison with all 10 — clarifying what the parameter actually controls.

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 resource (the Suara Restaurant Index) and a specific domain (Instagram usage by restaurants and cafes across ten named cities) with concrete metrics listed. Scope alone separates it from own-account siblings like my_instagram_insights or post_stats, so an agent can pick it without ambiguity.

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?

Opens with an explicit trigger ('Use this when someone asks how restaurants and cafes use Instagram in...') and adds a clear exclusion ('do not use it to promise results'). It doesn't name a sibling alternative for narrower own-account questions, which keeps it just short of a 5.

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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