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Google Play Console MCP

Google Play Console MCP

by abd3lraouf-studios
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Python
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Search Error Reports

search_error_reports

Retrieve raw crash and ANR reports with stack traces for an app package from Google Play Console, filtered by time and error criteria, to diagnose failures.

Instructions

Search raw error reports — individual crashes and ANRs with stack traces.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "package_name": "<package_name>"
    },
    "name": "search_error_reports"
  }
}

Input Schema

NameRequiredDescriptionDefault
days_backNoWindow size ending now, in days (default 7)
max_resultsNoMaximum number of reports to return (default 50)
package_nameYesApp package name (e.g., com.example.myapp)
filter_expressionNoOptional API filter (e.g., 'errorIssueId = abc123')

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose that results are raw, individual crashes/ANRs containing stack traces, which is valuable. However, it does not mention access requirements, result ordering, volume characteristics, or how results relate to issue-level aggregation.

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?

One front-loaded sentence with the essential resource and scope, followed by an em-dash detail. There is no filler, no repetition of the title, and every word contributes to understanding.

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 that all parameters are fully documented in the schema and an output schema exists, the description only needs to clarify the resource and granularity, which it does. The main missing piece—choosing between raw reports and issue-level search—is captured under usage_guidelines.

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%, with defaults and example filter expressions already documented. The tool description adds no parameter-level meaning, so the schema does the heavy lifting and the baseline 3 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?

States a specific verb and resource: 'Search raw error reports' followed by concrete content details ('individual crashes and ANRs with stack traces'). The word 'raw' and 'individual' help distinguish this from sibling search_error_issues, which likely operates at the aggregated issue level.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives such as search_error_issues or get_crashes_report. The description implies granularity but never states the trade-off between raw reports and aggregated issue summaries, leaving the agent to infer usage from sibling names and the word 'raw'.

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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