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Get slot demand

get_slot_demand

Read-only

Get per-slot search-demand: aggregate + Country x Month matrix.

Aggregate: volume_12m, prev_12m, yoy_pct, trend ('growing'|'declining'|'flat'), sparkline.
Matrix (Phase D2):
  months: up to 12 'YYYY-MM' labels, newest first (months[0] = freshest month in the data).
  totals: total demand per month across all countries, aligned to months (the trend line).
  markets: per-country breakdown, each {country, country_iso, flag, values (aligned to
    months), mom_delta (freshest-vs-previous month growth), volume_12m, trend}. Sorted by
    month-over-month growth descending — top rows are the fastest-growing markets.
Use months+markets to see where a slot is in demand and where it is growing or declining.
Empty snapshot (no demand data yet) returns 200 with nulls and months/totals/markets [], not 404.
slug: URL-friendly unique slot identifier.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedJul 19, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, so the safety profile is covered. The description adds extra behavioral context: 'Empty snapshot (no demand data yet) returns 200 with nulls and months/totals/markets [], not 404.' It also details sorting (growth descending) and field alignment, which go beyond annotation info.

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?

The description is detailed but efficiently structured with line breaks and clear sections. Every sentence adds value, including field definitions, alignment, sorting, empty behavior, and slug explanation. It is somewhat long, but the complexity of the return structure justifies the length.

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?

Despite having an output schema, the description explains the semantics of fields (e.g., months[] newest first, totals aligned to months, markets sorted by growth), which is not fully captured in a schema. It also covers edge cases (empty snapshot) and practical usage. For a tool with a complex nested result, this is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has one required parameter 'slug' with no description in the schema (coverage 0%). The description compensates by defining it: 'slug: URL-friendly unique slot identifier.' This fully clarifies the parameter's meaning and format, which is essential since the schema gives no other help.

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 opens with 'Get per-slot search-demand: aggregate + Country x Month matrix.' This uses a specific verb ('Get'), resource ('per-slot search-demand'), and clearly distinguishes it from sibling tools like get_provider_demand (per-provider) and get_demand_cut. The scope is unambiguous.

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?

The description provides clear usage context, e.g., 'Use months+markets to see where a slot is in demand and where it is growing or declining.' It explains how to interpret the data. However, it does not explicitly mention when to use this tool instead of alternatives like get_demand_cut, so it misses exclusions or alternative comparisons.

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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