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atlaspi-mcp

atlaspi-mcp

by Soil911
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

dataset_stats

Get aggregate statistics of the AtlasPI dataset: total entities, type breakdown, status and continent distributions, time range, average confidence, cited sources, and territorial changes. Understand dataset coverage before querying.

Instructions

Restituisce statistiche aggregate del dataset AtlasPI: numero totale di entita', breakdown per tipo, distribuzione per status (confirmed / uncertain / disputed), distribuzione per continente, intervallo temporale coperto (year_range), confidence_score medio, numero totale di fonti citate e di cambi territoriali. Utile come tool di scoperta iniziale per capire la copertura del dataset prima di formulare query specifiche.

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": "dataset_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.10.3

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description does not mention behavioral traits like read-only nature, performance implications, or rate limits. It focuses on output content but omits side-effect disclosure. A 3 is appropriate because the tool is simple and likely safe, but transparency could be improved.

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?

Single paragraph, front-loaded with the most important information (what it returns). Every sentence serves a purpose, no fluff. Very efficient.

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 no output schema, the description adequately lists the returned statistics. However, it does not specify if the stats are for the entire dataset or include any implicit filtering. For a zero-param discovery tool, it is mostly complete but could note response format or caching.

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?

No parameters exist, so schema coverage is 100%. The description adds value by explaining the output structure (list of statistics) beyond the empty schema. Baseline for zero params is 4, and the description meets that.

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 tool returns aggregate statistics for the AtlasPI dataset, listing specific metrics (total entities, breakdown by type/status/continent, time range, etc.). It distinguishes its role as a discovery tool before specific queries, setting it apart from sibling tools that focus on specific entities or events.

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?

Explicitly says 'Utile come tool di scoperta iniziale per capire la copertura del dataset prima di formulare query specifiche', indicating when to use it. Missing explicit when-not-to-use or alternatives, but the context makes it clear this is a high-level overview.

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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