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Apify job-search tools incur significant token costs for output schemas

A recent analysis of 20 job-search Actors on the Apify platform revealed significant token costs associated with tool definitions, particularly for large output schemas. The author measured the token count for each Actor's description, input schema, and inferred output schema, finding that output schemas can comprise the majority of the token usage. This cost is incurred before an agent even processes a user's query, highlighting a potential area for optimization in agent development. AI

IMPACT Highlights potential cost inefficiencies in agent tool integration, particularly concerning large output schemas.

RANK_REASON Analysis of tool costs on a specific platform.

Read on dev.to — MCP tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Apify job-search tools incur significant token costs for output schemas

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Signal score
12 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
Analysis of tool costs on a specific platform.
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, product
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — MCP tag TIER_1 English(EN) · Kevin Hall ·

    MCP tool cost: 20 job Actors on Apify, 2.1K to 32.7K tokens each

    <p>Here is what an agent pays before it asks a job-search tool anything. I pinned 20 job-search Actors from the Apify Store, one at a time, on the hosted Apify MCP server and read what <code>tools/list</code> sends back. The smallest tool definition was 2,139 tokens. The median w…