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DataLikers — Instagram & TikTok Data

get_stats

Read-onlyIdempotent

Get approximate record counts for the whole DataLikers database: Instagram users, posts, comments, stories, highlights, hashtags, locations, music tracks, and TikTok users, videos, comments, hashtags, playlists (keys total_users ... total_tt_playlists). Use it to gauge dataset coverage or answer 'how much data do you have'; it returns no individual records, use the search/get tools for those. Counts come from table statistics, so they are estimates, not exact totals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive semantics, so the safety profile is settled. The description adds genuinely non-obvious context beyond annotations: counts derive from table statistics and are estimates rather than exact totals. It does not discuss performance or freshness, keeping it short of a 5.

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 core purpose, then the usage cue, then the caveat. The long enumeration of entity types and keys is informative but bulky; it could be trimmed without losing much.

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?

With no output schema, the description usefully signals the return shape (approximate count keys such as total_users ... total_tt_playlists) and warns that values are estimates. An agent has enough to call and interpret it; only the full key list is left implicit.

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 tool takes zero parameters, so the baseline is 4. The description does not need to document inputs; referencing the count keys is a bonus rather than a substitute for parameter documentation.

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 (get) and resource (approximate record counts) for the whole database, and enumerates the covered entity families. It is clearly distinguishable from the many search_/get_*_by_* siblings because it operates on aggregates, not individual records.

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

Usage Guidelines5/5

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

Explicitly names both the intended use ('gauge dataset coverage', answer 'how much data do you have') and the exclusion ('it returns no individual records, use the search/get tools for those'). The agent is routed to alternatives with the condition that selects them.

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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