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spinnerich

geizhals-mcp

by spinnerich
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for searching products by keyword, the other for retrieving detailed offers for a specific product. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the verb_noun pattern (search_products, get_product), which is consistent and predictable.

    Tool Count3/5

    Only two tools for a price comparison service seems minimal. While the core workflow is covered, additional tools for categories or price history would enhance the surface.

    Completeness3/5

    Missing features like category browsing, price alerts, or multiple product comparison. However, the basic search and detail retrieval are present.

  • Average 4.1/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 8 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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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.

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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

  • Behavior3/5

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

    The description discloses that the tool returns a list of matching products with lowest price, number of offers, and page URL, but does not mention any side effects, rate limits, or additional behavioral traits. Since annotations are absent, the description carries the burden but lacks detail like whether it's read-only.

    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 4 lines, well-structured, and front-loaded with the purpose. Every sentence adds value: purpose, sort options, price range, and return structure. No redundant or unnecessary text.

    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 complexity (5 params, has output schema, sibling tool), the description covers the main aspects: purpose, key parameters, and return value. It lacks explanation for 'limit', but with the output schema clarifying return fields, it is mostly complete.

    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 0%, so the description must add meaning. It explains 'sort' (relevance/price_asc/price_desc) and 'min_price'/'max_price' (euro range). 'query' is implied by 'search by name or keyword', but 'limit' is not described, leaving some parameter semantics incomplete.

    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 geizhals.de for products by name or keyword, which is a specific verb and resource. It distinguishes from the sibling tool 'get_product' which likely retrieves a single product's details.

    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 provides context on using sort and price range parameters but does not explicitly state when to use this tool versus the sibling 'get_product' or any conditions for not using it. The guidance is implied but not explicit.

    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 must provide behavioral traits. It discloses that results include shop offers (sorted cheapest first), price range, and price history. This is good transparency, though it could mention pagination or limits.

    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 two sentences long, front-loaded with the action, and every word provides value. No extraneous 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 the presence of an output schema (indicated true), the description adequately explains input and key output aspects. It covers input format and return values (offers, price range, history). A mention of the output schema reference would slightly improve completeness.

    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 single parameter 'product' has 0% schema coverage, meaning the schema provides no description. The description adds critical meaning: it accepts a geizhals product URL or numeric product ID, which is essential for correct invocation.

    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 looks up offers for a single product using a URL or numeric ID. It distinguishes itself from the sibling tool search_products, which implies searching for 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 explicitly states it is for a single product, but does not provide explicit when-not-to-use guidance or alternative tool references. However, the context is clear enough for an agent to infer usage.

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

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