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PinMeTo Location MCP

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by PinMeTo

Google review analysis

pinmeto_get_google_review_insights
Read-only

Summarize Google reviews into rating and sentiment statistics, returning token-efficient aggregates rather than raw review text. Supports summary, comparison, and large-dataset sampling.

Instructions

Summarize Google reviews into rating and sentiment statistics.

Computes aggregates over the matched reviews server-side and returns the summary instead of the raw reviews, which is far more token-efficient than fetching them. The returned data is descriptive statistics, not LLM-written prose: read it and draw your own conclusions for the user.

Analysis Types:

  • summary: Average rating, sentiment breakdown, rating distribution

  • comparison: The above, plus per-location metrics ranked by rating

  • issues, trends, themes: Accepted, but currently return the same payload as summary. No theme extraction, issue clustering, or period comparison is performed, and the response is flagged with warningCode UNDIFFERENTIATED_ANALYSIS_TYPE. To analyze review text, fetch pinmeto_get_google_reviews and read it yourself.

Large Dataset Handling:

  • <200 reviews: Processed immediately

  • 200-1000: Processed with token estimate in metadata

  • 1000-10000: Returns warning with options (set skipConfirmation=true to proceed)

  • 10000: Requires sampling strategy (representative or recent_weighted)

Sampling Strategies (which reviews get analyzed):

  • full: Analyze all reviews (default for <10000 reviews)

  • representative: Stratified sample by rating and location

  • recent_weighted: Prioritize recent reviews

Caching:

  • Results cached for 1 hour

  • Use forceRefresh=true to bypass cache

When NOT to use this tool:

  • Need raw review text: Use pinmeto_get_google_reviews

  • Need only aggregate stats: Use pinmeto_get_google_ratings

  • Need specific review lookup: Use pinmeto_get_google_reviews with filters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesEnd date (YYYY-MM-DD)
fromYesStart date (YYYY-MM-DD)
themesNoCurrently ignored - no theme extraction is performed
storeIdsNoOptional store IDs to analyze (omit for all locations)
maxRatingNoMaximum rating filter (1-5)
minRatingNoMinimum rating filter (1-5)
analysisTypeYesType of analysis: summary, issues, comparison, trends, or themes
forceRefreshNoBypass cache and regenerate analysis
response_formatNoResponse format: "json" (default, token-efficient) or "markdown" (human-readable with tables)json
samplingStrategyNoSampling strategy: full (default), representative, or recent_weightedfull
skipConfirmationNoSkip large dataset confirmation (set true after receiving warning)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoAnalysis results (null or absent on error, warning, or no matching reviews)
errorNoError message if the request failed
warningNoWarning message
metadataNoAnalysis metadata
errorCodeNoError code for programmatic handling
retryableNoWhether the operation can be retried
warningCodeNoWarning code for programmatic handling
analysisNoteNoNote about the analysis itself, present when there is no metadata to carry it
largeDatasetWarningNoLarge dataset details and options (when requiresConfirmation is true)
requiresConfirmationNoTrue if user must confirm before proceeding with large dataset
Behavior5/5

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

Annotations only declare readOnlyHint=true, so the description carries the burden of behavioral disclosure. It reveals critical behaviors: server-side aggregation, token efficiency, caching with forceRefresh, warning flags for undifferentiated types, and the multi-tier large-dataset confirmation flow. This adds substantial context beyond the read-only hint.

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 longer than average but justified by the tool's complexity. It is well-structured with headers and bullets, and each section adds actionable detail. Slight redundancy (e.g., theme extraction limitation appears twice) prevents a perfect score, but it's far from bloated.

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 11 parameters, large-dataset handling, and multiple analysis modes, the description covers all critical usage scenarios: data volume behavior, sampling, caching, and output nature. An output schema exists, so return-value details are not needed. No gaps are evident.

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?

Schema coverage is 100%, so baseline is 3. The description adds valuable nuance: analysisType values like 'issues' and 'trends' return the same payload as summary, themes is ignored, skipConfirmation is used after a warning, and sampling strategies have specific meanings. This enriches the plain schema enum and boolean definitions.

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 a clear verb+resource statement: 'Summarize Google reviews into rating and sentiment statistics.' It goes on to list distinct analysis types (summary, comparison, issues, etc.) and contrasts with sibling tools like pinmeto_get_google_reviews and pinmeto_get_google_ratings, making the tool's unique role unmistakable.

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 includes an explicit 'When NOT to use this tool' section naming three sibling tools and the exact conditions under which each should be preferred. It also explains when to use different analysis types and large-dataset options, giving clear selection guidance.

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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