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keyword-research

Plan and save keyword research data for app store optimization across multiple locales and platforms, ensuring structured execution and data persistence.

Instructions

Prep + persist keyword research ahead of improve-public using mcp-appstore outputs.

IMPORTANT: Always use 'search-app' tool first to resolve the exact slug before calling this tool. The user may provide an approximate name, bundleId, or packageName - search-app will find and return the correct slug. Never pass user input directly as slug.

CRITICAL: Multi-Locale Execution Plan

MANDATORY WORKFLOW - Complete each locale fully before moving to next:

For EACH locale+platform combination, execute this cycle:

  1. Plan: Call keyword-research(slug, locale, platform) with writeTemplate=false → get research plan

  2. Research: Execute COMPLETE mcp-appstore workflow (all 16 steps) for that locale

  3. Save: Call keyword-research again with researchData or researchDataPath → persist actual data

  4. Next: Move to next locale+platform and repeat steps 1-3

IMPORTANT: Research → Save → Next pattern

  • Complete ONE locale fully (research + save) before starting the next

  • This prevents data loss if the session is interrupted

  • Each locale's data is persisted immediately after research

FORBIDDEN:

  • ❌ Using writeTemplate=true as final output

  • ❌ Skipping secondary locales

  • ❌ Researching multiple locales then saving all at once at the end

  • ❌ Stopping before all locale+platform combinations are done

REQUIRED:

  • ✅ Research locale → Save locale → Move to next (one at a time)

  • ✅ Run for EVERY platform (ios AND android separately)

  • ✅ Use researchData or researchDataPath to save (NOT writeTemplate)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesProduct slug
localeYesLocale code (e.g., en-US, ko-KR). Used for storage under .aso/keywordResearch/products/[slug]/locales/.
platformNoStore to target ('ios' or 'android'). Run separately per platform.ios
countryNoTwo-letter store country code. If omitted, derived from locale region (e.g., ko-KR -> kr), else 'us'.
seedKeywordsNoSeed keywords to start from.
competitorAppsNoKnown competitor apps to probe.
filenameNoOverride output filename. Defaults to keyword-research-[platform]-[country].json
writeTemplateNoIf true, write a JSON template at the output path.
researchDataNoOptional JSON string with research results (e.g., from mcp-appstore tools). If provided, saves it to the output path.
researchDataPathNoOptional path to a JSON file containing research results. If set, file content is saved to the output path (preferred to avoid escape errors).
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly describes behavioral traits: the multi-step workflow (plan, research, save), persistence of data to prevent loss, platform-specific execution (ios and android separately), and critical constraints like forbidding 'writeTemplate=true' as final output. This covers operational context, safety, and procedural requirements beyond basic functionality.

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 well-structured with clear sections (IMPORTANT, CRITICAL, FORBIDDEN, REQUIRED) and uses bullet points for readability. While lengthy due to detailed workflows, every sentence earns its place by providing essential guidance, such as the mandatory execution plan and data persistence rules, without redundancy. Minor improvements could streamline some repetitive points.

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?

Given the tool's complexity (10 parameters, no annotations, no output schema), the description is highly complete. It explains the tool's role in a broader workflow, provides step-by-step usage instructions, details behavioral constraints, and addresses potential pitfalls like data loss. This compensates for the lack of structured annotations and output schema, ensuring the agent can use the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 10 parameters comprehensively. The description adds minimal parameter-specific semantics beyond the schema, such as implying 'slug' usage with 'search-app' and workflow context for 'writeTemplate', 'researchData', and 'researchDataPath'. However, it doesn't provide additional syntax or format details, aligning with the baseline score when schema coverage is high.

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's purpose: 'Prep + persist keyword research ahead of improve-public using mcp-appstore outputs.' It specifies the verb ('prep + persist'), resource ('keyword research'), and distinguishes it from siblings by mentioning its relationship to 'improve-public' and 'mcp-appstore outputs', making it distinct from tools like 'search-app' or 'aso-to-public'.

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?

The description provides explicit guidance on when to use this tool vs alternatives. It mandates using 'search-app' first to resolve slugs, details a multi-locale execution plan with required and forbidden workflows, and specifies the tool's role in a larger process involving 'mcp-appstore' outputs, clearly differentiating it from other tools like 'improve-public' or 'validate-aso'.

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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