Polvenn MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.2
Disambiguation4/5
Most tools are distinct, but the three search tools (search_upcoming_beers, search_new_beers, search_new_beers_near_store) have overlapping functionality, especially since search_new_beers can include upcoming results. Descriptions clarify differences, but there's a risk of selecting the wrong one.
Naming Consistency4/5
Tool names follow a consistent pattern with the 'polvenn_' prefix and verb_noun format (e.g., check_store_stock, search_new_beers). The exception is 'polvenn_watchlist' which uses a noun instead of a verb action, but it's still clearly named.
Tool Count5/5
With 8 tools, the server is well-scoped for its purpose: searching new/upcoming beers, checking stock, finding stores, managing a watchlist, and configuration. Each tool serves a distinct need without being excessive.
Completeness4/5
The domain of beer release tracking and store stock is well covered, including search, stock, store location, watchlist, and config. However, there's no general search for all beers, only new/upcoming, which may be a gap for users wanting to look up existing products.
Average 4.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context beyond these by explaining the 'best-effort date filter', the ability to include upcoming releases via 'Kommende nyheter', and the return format ('List of new beers with name, producer, style, ABV, article number, and release date'). This goes further than the annotations alone.
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 front-loaded with a one-sentence purpose, followed by a structured Args list and Returns line. It is not overly verbose but does repeat parameter information already present in the schema. Still, the structure is clear and scannable, and every sentence contributes to understanding the tool.
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 7 parameters are optional and the schema fully explains them, and there is no output schema, the description compensates by including a Returns section that lists the output fields. It covers the core functionality well, though it omits potential edge cases, error conditions, or pagination details. For a read-only search tool, this is adequate.
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 every parameter thoroughly. The Args list in the description adds no new meaning beyond the schema; in fact, it omits some schema details (e.g., the Norwegian date format for releaseDate). Since the baseline is 3 for full schema coverage, this score 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 opens with a clear, specific statement: 'Search for recently released beers on Vinmonopolet via the official API and/or your external release feed.' This names the verb (search), the resource (recently released beers on Vinmonopolet), and the data sources, distinguishing it from siblings like search_upcoming_beers or search_new_beers_near_store.
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 usage by stating what it searches, but it does not explicitly specify when to prefer this tool over alternatives, nor does it mention any exclusions or alternative tool names. For example, it doesn't say 'For upcoming beers, use search_upcoming_beers' or 'For store-specific queries, use check_store_stock'.
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?
The annotations already communicate that this is a safe, read-only, idempotent operation, so no safety disclaimers are needed. The description adds context about the data being from a live website listing and includes 'current website status' in the returns, providing some behavioral insight beyond the annotations. No contradictions exist.
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 compact paragraph followed by a clear parameter list and a return note. It is front-loaded with the primary purpose and avoids redundancy, making it easy to parse.
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?
With no output schema, the description helpfully lists the expected return fields (name, producer, style, ABV, article number, website status). It also covers parameters and purpose. However, it omits any mention of configuration prerequisites or dependencies on other tools, which is a notable gap considering the sibling tools include polvenn_configure and polvenn_validate_config.
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?
Both parameters (style and limit) are fully documented in the input schema with types, defaults, and examples. The tool description essentially restates these, adding only a slightly less detailed example for style. Given the high schema coverage, the description adds minimal additional semantic value, hence a baseline score of 3.
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 explicitly identifies the resource (Vinmonopolet's 'Kommende nyheter' web listing) and the action ('Search upcoming beer releases'), which distinguishes it from sibling tools like polvenn_search_new_beers that target different listings. The verb 'Search' is concrete and the scope is clear.
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 clearly indicates the tool is for finding upcoming beer releases, giving an agent a clear context for when to invoke it. However, it falls short of explicitly naming alternative tools for other scenarios (e.g., search_new_beers for current releases), so it earns a 4 rather than a 5.
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 annotations already indicating readOnlyHint=false and openWorldHint=true, the description adds useful context by stating that the tool tracks rules and gets users notified about matching releases. It discloses the action variants but does not detail side effects of 'check' or removal consequences beyond the schema. The annotations cover safety; the description adds moderate behavioral context.
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 concise and well-structured: a clear one-sentence summary followed by a structured Args list. No wasted words, and the format makes the action-parameter relationships easy to scan.
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?
This multi-action tool has no output schema, so the description should ideally explain return values or side effects. It defines actions and their inputs but does not clarify what 'list' or 'check' return, nor whether 'check' triggers notifications immediately. Despite this gap, the description covers the core purpose and parameter dependencies well for the tool's moderate complexity.
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 every parameter described, so the baseline is 3. The description's Args block restates the parameter conditions (e.g., 'required for add') but adds no new meaning beyond what the schema already provides. It is helpful grouping but not compensatory.
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 'Manage your beer watchlist', a specific verb and resource, and further specifies trackable entities (breweries, styles, series, keywords). This clearly differentiates it from sibling tools focused on store stock and beer search.
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 explains the available actions (add/remove/list/check) but provides no explicit guidance on when to prefer this tool over siblings like search_new_beers or check_store_stock. Usage context is implied through the watchlist domain, but no alternatives or exclusions are mentioned.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral detail beyond these: the storeId fallback chain ('Falls back to your configured home store, then your nearest store from your home coordinates') and the enrichment behavior ('enriched with external release dates when recent feed items match'). This extra context helps the agent understand what happens under the hood.
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 concise and well-structured: a one-sentence purpose, an Args list, and a Returns line. However, the Args list partially duplicates the schema descriptions, which is slightly redundant. Still, it's efficient and front-loaded with the key purpose.
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 an output schema, the description explains the return value ('New beers currently available in the selected store, enriched with external release dates when recent feed items match'). It also covers parameter fallback behavior and the filtering options. It could be more verbose about the exact fields returned, but for a read-only search tool with strong annotations, this is adequate.
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 each parameter already well-described in the JSON schema (e.g., releaseDate accepts 'yyyy-MM-dd or a Norwegian date like "1. april 2026"'). The description's Args section largely duplicates this information without adding new semantics. Since the schema carries the heavy lifting, a baseline score of 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's function: 'Search Vinmonopolet's current "Nyheter" listing for beers available in one store.' It specifies the resource (Vinmonopolet's Nyheter), the action (search), and the unique scope (store-specific), which distinguishes it from sibling tools like polvenn_search_new_beers (which lacks store filtering) and polvenn_check_store_stock (which checks stock for specific products).
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 for when to use this tool: when you want new beers from a specific store, with fallback logic for store selection. However, it does not explicitly mention alternatives or when not to use it, relying instead on the name and sibling context.
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?
Description adds useful behavior beyond annotations: mentions return contents (stock status and product details) and the default store behavior. Annotations already declare read-only/idempotent/destructive hints, so this supplemental context is appropriate and not contradictory.
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?
Description is compact and front-loaded: purpose, args, returns in four lines. No filler or redundant explanation beyond a useful summary.
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 read-only two-parameter tool, the description covers purpose, required/optional inputs, and return type. It does not specify exact stock status values or error cases, but these are not essential given the tool's simplicity and annotations.
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 already documents both parameters with 100% coverage, including the optional storeId default and an example article number. The description's Args section largely restates schema info without adding new semantic details.
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?
Clearly states the tool's function with a specific verb 'Check' and resource 'stock at a Vinmonopolet store'. It distinguishes from sibling search/config tools by focusing on checking availability for a specific article at a specific store.
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?
Description provides context that the tool is for checking stock and clarifies that storeId defaults to the home store. It does not explicitly mention when not to use it or alternative tools, but the intended use is clear and no exclusions are needed given the sibling tools' distinct purposes.
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?
Annotations already declare read-only, idempotent, and non-destructive. The description adds useful context: latitude/longitude default to configured home location, and the return includes address, distance, category, and opening hours. This goes beyond the annotations.
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 an Args/Returns layout and front-loaded purpose. However, the Args list largely duplicates schema descriptions, which is slightly redundant but not wasteful enough to drop below 4.
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 read-only tool with no output schema, the description adequately explains return values (list of stores with address, distance, category, opening hours) and optional parameter defaults. No critical behavioral details are missing for an agent to use it 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 parameters. The description repeats the defaults (home location, maxResults=5) without adding new semantics about formatting, constraints, or edge cases, 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 states 'Find the closest Vinmonopolet stores to a given location' with a specific verb and resource, clearly distinguishing from siblings focused on beer stock/new releases. The return list of stores with address, distance, etc. reinforces the purpose.
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?
It provides clear context for when to use (nearby store lookup) and what it returns, but does not explicitly mention alternatives or when not to use it. The sibling polvenn_search_new_beers_near_store could be confused, but the description's focus on store details differentiates it.
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?
Annotations already express idempotency and non-destructiveness. The description adds that only supplied fields are modified and that the returned config masks keys, providing useful behavioral context (partial updates, security-sensitive output) beyond the structured metadata.
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 compact two-part structure: a one-sentence action summary followed by a scannable bullet list of arguments and a return note. Every element earns its place with no redundant wording.
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?
Covers purpose, parameters, return value, and the partial-update nuance; the masked-keys note is an important extra for an agent's trust evaluation. Given no output schema or nested objects, this is sufficiently complete for an AI 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?
Schema covers 100% of parameters with descriptions, giving a baseline of 3. The description enriches semantics by explaining 'only provided values are updated' and adds source hints (e.g., vinmonopoletApiKey from api.vinmonopolet.no), going beyond the schema's simple field names and descriptions.
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 states 'Set the release feed URL, API key, home store, and location for the Polvenn MCP server' with a clear verb and target resource. This clearly distinguishes polvenn_configure from sibling tools like polvenn_validate_config or polvenn_find_nearby_stores.
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 notes 'Args (all optional — only provided values are updated)', giving clear context for when to use it (config updates) and the partial-update behavior. It does not explicitly contrast against alternatives, but the configuration purpose is evident, so it meets the 'clear context, no exclusions' bar.
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?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by listing specific checks and the return format (pass/warn/fail), providing context beyond the 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 concise and well-structured: a single opening sentence, a bulleted list of checks, and a closing line about the return type. Every element adds value with no waste.
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 no-parameter tool with no output schema, the description fully covers what the tool does, what it checks, and what it returns. No critical information is missing.
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?
With zero parameters, the baseline is 4. The description doesn't need to explain parameters, and it doesn't, which 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 states a specific verb ('Validate') and resource ('Polvenn configuration'), followed by concrete checks. It clearly distinguishes from sibling tools that focus on searching or stock checking.
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 context is clear: this tool is for validating configuration and checking service accessibility. It doesn't explicitly exclude alternatives or name when-not-to-use, but its purpose is distinct enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcp-polvenn MCP server](https://glama.ai/mcp/servers/henrikogaard/mcp-polvenn/badges/card.svg)](https://glama.ai/mcp/servers/henrikogaard/mcp-polvenn)

Score Badge

Copy to your README.md:

[![mcp-polvenn MCP server](https://glama.ai/mcp/servers/henrikogaard/mcp-polvenn/badges/score.svg)](https://glama.ai/mcp/servers/henrikogaard/mcp-polvenn)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/henrikogaard/mcp-polvenn'

If you have feedback or need assistance with the MCP directory API, please join our Discord server