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tokscale_market_share

Read-only

Analyze local AI tool usage data to compute market share by tokens, cost, and messages. Ranks tools by token share for workflow dominance insights.

Instructions

Complete AI tool market share analysis from your local tokscale data. Aggregates per-model usage by client (AI tool) and computes each tool's share of total tokens, cost, and messages. Returns each tool ranked by token share, with share_tokens / share_cost / share_messages percentages and a totals rollup. All data is read locally from tokscale's scan of your session logs — no network calls, no PII. Use this to see which AI coding tools dominate your workflow by volume, spend, or activity. Do NOT use this for per-model detail — use tokscale_developer_profile for that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent if tokscale is unavailable
toolsNoAI tools ranked by token share, each with { client, label, tokens, cost, messages, model_count, share_tokens, share_cost, share_messages }
totalsNoAggregate totals: { tokens, cost, messages, tool_count }
Behavior5/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. Description adds that data is local, no network calls, no PII, and lists output components (shares, totals). No contradictions.

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?

Concise paragraph covering purpose, usage, behavioral notes, and output specification without unnecessary words.

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?

For a 0-parameter tool with output schema and good annotations, the description adds all necessary context: purpose, usage, data scope, and output summary.

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?

Input schema has 0 parameters with 100% coverage. Description states 'No parameters' which is clear. Baseline 4 is appropriate.

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?

Clearly states it provides market share analysis from local data, aggregates per-model usage, computes shares, and distinguishes from sibling tool for per-model detail.

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 says when to use (see dominant tools) and when not to use (for per-model detail, use tokscale_developer_profile).

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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