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aadilr

CostBench MCP Server

by aadilr
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

75%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one surfaces hidden costs, the other provides negotiation tactics. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent 'get_' prefix with descriptive noun phrases ('hidden_costs', 'negotiation_tips'), forming a clear and predictable pattern.

    Tool Count3/5

    Only two tools is minimalist but reasonable for a highly specialized server focused on cost revelation and negotiation tips. However, the narrow scope may limit usability in broader cost analysis workflows.

    Completeness3/5

    The tools cover two key aspects of cost benchmarking—hidden costs and negotiation—but lack potentially important operations like listing products, comparing prices, or tracking cost changes over time.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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.

  • This repository includes a glama.json configuration file.

  • 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 indicate readOnlyHint=true, and the description confirms the tool is for retrieving tips. Additionally, it adds behavioral context by mentioning 'success-likelihood', which hints at probabilistic or scoring output, going 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 a single sentence that quickly conveys the purpose. It is front-loaded and free of fluff, though it could benefit from slightly more detail without becoming verbose.

    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?

    For a simple tool with one parameter and no output schema, the description is minimal. It fails to clarify the 'slug' parameter or the format of 'success-likelihood', but the overall purpose is understandable.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The only parameter 'slug' has no description in the schema (0% coverage) and the tool description does not explain what it represents (e.g., product identifier). This leaves the agent uncertain about how to provide input.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool provides negotiation tactics for a product, specifically how to get a discount. However, it does not explicitly differentiate from the sibling tool 'get_hidden_costs', leaving some ambiguity about when to choose one over the other.

    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 guidance is provided on when to use this tool versus alternatives like 'get_hidden_costs'. The agent must infer usage from the description alone, which lacks explicit when-to-use or when-not-to-use instructions.

    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?

    The annotations already declare readOnlyHint=true, so the description is not required to restate that. The description adds value by specifying the output includes sourced quotes and dates, which goes beyond the structured annotations. No contradictions with annotations. However, it could mention that results are limited to documented costs.

    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 extremely concise—two sentences totaling ~160 characters. It is front-loaded with the key purpose and output details, with 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.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's simplicity (one required parameter, no output schema, one sibling), the description omits crucial information: what 'slug' means, how to obtain it, and whether results are paginated or limited. The agent cannot reliably invoke the tool without understanding the input parameter.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has one parameter 'slug' with no description and 0% schema coverage. The tool description does not explain what 'slug' represents (e.g., product identifier, URL slug). The agent has no guidance on how to determine the correct value, making effective use difficult.

    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 returns hidden/implementation/add-on costs of a product, each with a sourced quote and date. It uses a specific verb ('get') and resource ('hidden costs'), and the phrase 'what they don't tell you' answer adds clarity. It distinguishes from the sibling tool 'get_negotiation_tips' by focusing on costs rather than tips.

    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?

    The description does not provide explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or compare to 'get_negotiation_tips'. The agent must infer usage solely from the tool name and brief description.

    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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