partnerlens-mcp

Official

by seven-hills-software

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation: comparing apps, fetching details, reviews, summaries, searching, listing categories, top apps, and private metrics. No overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_app, list_categories, search_apps), making it easy for agents to infer functionality.
Tool Count5/5
Nine tools cover the domain of Shopify app analytics and partner metrics comprehensively without unnecessary bloat or missing essentials.
Completeness5/5
The set provides full lifecycle coverage: discovery (search, categories, top apps), detailed info (get_app), reviews (get_app_reviews, get_review_summary), comparison (compare_apps), and partner private metrics (get_my_apps, get_my_metrics). No obvious gaps.
Average 4.2/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the action but does not disclose behavioral traits such as read-only nature, error handling for invalid slugs, or any side effects. The description is insufficient for a tool without annotations.
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?
The description is a single sentence plus a short tagline, both concise and front-loaded. Every part adds value: the core action, the scope, the output, and a usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description covers the purpose and input range. However, it lacks any detail about the output format or behavior on invalid input, which are gaps for a tool without an output schema.
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 description coverage is 100% with the 'slugs' parameter well-documented (pattern, note about search_apps). The description adds meaning beyond the schema by specifying the comparison includes metadata and review summaries, and the use case for competitor analysis.
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 compares 2-5 Shopify apps side by side, listing what is compared (metadata and aggregate review summary). It distinguishes itself from siblings like get_app (single app) and get_app_reviews (reviews for one app) by specifying the comparative nature and the number of apps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes 'Ideal for competitor analysis,' which provides context for when to use the tool. However, it does not explicitly state when not to use it or name alternative tools. The context is clear but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It lists outputs but does not disclose authentication needs, rate limits, or side effects. The behavior is implied to be read-only but not explicitly stated.
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?
Two sentences with no wasted words. The first sentence lists the outputs and the second gives a usage suggestion. Perfectly concise.
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 the single parameter with full schema coverage and no output schema, the description lists the return values adequately. It provides context with sibling tools and suggests this as a starting point, but could mention error handling or data availability.
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?
The parameter 'slug' has 100% schema description coverage with a clear explanation. The tool description does not add extra meaning beyond the schema, so 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?
The description clearly states it aggregates review intelligence and lists specific analytics (rating distribution, sentiment, velocity, pain points). It distinguishes from raw reviews by saying 'Much cheaper than reading raw reviews' and implies it's for app health check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by saying 'start here for app health' and contrasting with raw reviews. However, it does not explicitly exclude other sibling tools like compare_apps or get_app.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full weight. It describes the dataset size and return fields, but lacks disclosure of potential behaviors like rate limits, pagination, or error handling. For a simple search, this is adequate but not rich.
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?
Two sentences, no wasted words. First sentence states purpose and scope, second provides actionable guidance. Perfectly concise.
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?
For a simple search tool with no output schema, the description covers the action, returned data, and usage context. It lacks details about response format or no-result handling, but is mostly complete given the tool's simplicity and the presence of sibling tools that provide further detail.
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 coverage is 100%, with both parameters already described in the input schema. The description adds context about return fields but does not enhance parameter meaning. 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?
The description clearly states the tool searches the Shopify App Store by app name, developer name, or slug, and specifies the returned fields (rating, review count, categories). It also distinguishes itself by indicating its role in resolving app names to slugs, which differentiates it from siblings like get_app that require a slug.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this first to resolve an app name to its slug,' providing clear guidance on when to use this tool before others. It does not mention when not to use or list alternatives, but the directive is strong enough for a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It states it reads reviews (read-only), pages, and orders newest first. It does not mention rate limits, authentication, or empty results, but the core behavior is clear. This is adequate, not exhaustive.
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?
Two sentences, each earning its place: the first defines purpose and ordering; the second adds filtering guidance. No superfluous words, and key information is front-loaded.
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?
Without output schema, the description could optionally mention return structure, but it covers the essential aspects: what it reads, filtering, paging, and ordering. Given the tool's simplicity, it is fairly complete.
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 description coverage is 100%, baseline 3. The description adds value by giving concrete examples for the rating parameter (e.g., complaints, churn reasons). This enhances understanding beyond the schema's generic description.
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 verb 'Read' and the resource 'individual reviews for a Shopify app', and specifies ordering 'newest first'. It distinguishes from siblings like search_apps and get_review_summary by focusing on individual reviews.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for using rating filters with examples (e.g., rating=1 for complaints, rating=5 for positive). It does not explicitly state when to use this tool versus siblings, but the examples imply usage for sentiment analysis. The schema note about using search_apps first is helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses private nature, plan requirement, and return metrics. Falls short of detailing exact behavior like read-only (implied), error states, or rate limits. Adequate but not comprehensive.
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?
Description is a single paragraph with no fluff. Every sentence adds information: what, defaults, requirements, and usage hint. Could be slightly more structured (e.g., separate prerequisites), but effective.
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?
Despite no output schema or annotations, the description covers all essential aspects: what metrics are returned, default time range, parameter behavior, prerequisites, and suggested combination with other tools. Comprehensive for a data-retrieval tool.
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 covers 100% of parameters, baseline 3. Description adds value beyond schema by explaining 'app' as 'Partner app id from get_my_apps (omit for all your apps)' and clarifying date defaults (30 days ago, today).
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?
Description uses specific verb 'get' and resource 'metrics' with clear scope ('YOUR OWN private daily Partner Dashboard metrics'). Distinct from sibling tools like 'get_top_apps' (public) and 'search_apps' (public), making selection unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides key context: defaults to last 30 days (max 366), requires PartnerLens Unlimited plan with Shopify integration, and suggests combining with public tools for comparison. Lacks explicit 'when not to use', but the contrast with sibling tools is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description adds context about plan requirement, sync dependency, and return behavior. However, lacks details on read-only nature, rate limits, or error states. Barely adequate given no annotations.
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?
Three sentences, front-loaded with purpose, then prerequisites, then usage hint. No unnecessary words, every sentence adds value.
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 zero-parameter, no-output-schema tool, description covers purpose, prerequisites, synchronization behavior, and next-step usage. Complete for agent to invoke correctly.
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?
Tool has zero parameters; schema covers 100%. Baseline score of 4 is appropriate as no additional parameter info needed.
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?
Description clearly states it lists the user's own apps with specific attributes (name, handle, partner app id). Distinct from siblings like get_app (single app) and search_apps (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Specifies prerequisites (PartnerLens Unlimited plan, integration connected) and behavior until sync. Also advises using returned id with get_my_metrics. Lacks explicit when-not-to-use but sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the data source ('from the latest scrape') and output fields, but doesn't mention auth needs, rate limits, or if data is cached. However, it adequately describes the tool's behavior for a read-only list operation.
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?
Two sentences, front-loaded with key information, no redundant words. Efficiently communicates purpose and output.
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 simple list tool with low complexity, the description is complete. It specifies input (category, optional limit), output contents, and data freshness. No output schema needed given the clear output description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds value by noting the category slug comes from list_categories and that limit defaults to 25, providing context beyond the schema.
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 retrieves current top-ranked apps in a Shopify App Store category, specifying it's from the latest scrape and listing output fields (position, app metadata, rating, review count). This distinguishes it from siblings like get_app (single app) and search_apps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when wanting top-ranked apps by category, but provides no explicit guidance on when not to use or compare to alternatives like search_apps. No exclusion criteria or usage context beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tool lists categories with counts and ordering. It does not describe side effects (none expected) or rate limits, but the behavior is simple and fully described.
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?
The description is a single sentence that conveys purpose and a follow-up usage hint. Every word earns its place; no filler.
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 zero parameters, no output schema, and a simple list return, the description is complete enough. It explains what is returned and how to use it. Could add detail on pagination or total count, but not necessary for this simple tool.
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?
The input schema has zero parameters, so the description does not need to add parameter details. The baseline of 4 is appropriate as the description adds value beyond the schema by explaining the output ordering and usage.
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 lists all Shopify App Store categories with app counts, sorted largest first. It differentiates itself from siblings by specifying a unique purpose and output usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on what to do with the output ('Use the returned slugs with get_top_apps'), which helps the agent understand the tool's role in a workflow. It lacks explicit when-not-to-use instructions, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description discloses returned fields but does not explicitly state read-only nature or any restrictions, though 'Get' implies safe operation.
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?
Single sentence, 22 words, front-loaded with action and output, no redundant information.
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 one parameter and no output schema, the description lists all returned fields and provides sufficient context for usage, though it lacks error handling info.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage for the single parameter with description; the tool description adds context by explaining the slug format and linking to search_apps, exceeding the schema alone.
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 'Get full details for one Shopify app by slug' and lists specific fields (description, developer, rating, etc.), distinguishing it from sibling tools like search_apps and get_top_apps.
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 advises 'Use search_apps first if you only know the app name,' providing clear when-to-use and alternative tool 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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