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BACH-AI-Tools

CEDAR MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_instances_based_on_template retrieves paginated instances of a template, while get_template fetches the template itself. There is no overlap in functionality, and an agent can easily differentiate between them based on their descriptions and intended use cases.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern: get_instances_based_on_template and get_template. They use snake_case uniformly, and the verb 'get' is applied consistently to indicate retrieval operations, making the naming predictable and easy to understand.

    Tool Count2/5

    With only 2 tools, this server feels too thin for its apparent domain of interacting with a CEDAR repository. While the tools cover template and instance retrieval, there are likely missing operations such as creating, updating, or deleting templates/instances, which limits the server's utility and scope.

    Completeness2/5

    The server is severely incomplete for a CEDAR repository interface. It only provides read operations (get_template and get_instances_based_on_template) with no support for create, update, delete, or other essential actions like searching or managing metadata. This leaves significant gaps that will hinder agent workflows and limit functionality.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the template data is 'cleaned and transformed', which adds useful context about post-processing behavior. However, it lacks details on authentication needs, rate limits, or error handling, which are important for a retrieval tool.

    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 appropriately sized and front-loaded with the main purpose, followed by structured sections for args and returns. Each sentence adds value, such as the example and transformation note, with no wasted words, though it could be slightly more streamlined.

    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 tool's low complexity (one parameter) and the presence of an output schema, the description is reasonably complete. It covers the purpose, parameter semantics, and return behavior, though it could benefit from more usage guidelines and behavioral details to be fully comprehensive.

    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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'template_id' can be an ID or full URL, provides an example, and clarifies the format, compensating well for the schema's lack of documentation. With only one parameter, this is effective.

    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 verb 'Get' and resource 'template from the CEDAR repository', making the purpose understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_instances_based_on_template', which appears to retrieve instances rather than templates, so this is a minor gap in sibling differentiation.

    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. The description mentions retrieving a template but doesn't clarify scenarios where this is preferred over other methods or tools, such as the sibling tool for instances, leaving the agent without usage 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: pagination support, chunking to avoid token limits, error handling (returns errors list), and the structure of the return value. However, it doesn't mention rate limits, authentication requirements, or performance characteristics.

    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 well-structured and front-loaded with the core purpose. Every sentence adds value: first states the action, second explains the pagination rationale, then clearly documents parameters and return structure. No wasted words or redundant information.

    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 moderate complexity (3 parameters, pagination logic) and the presence of an output schema (implied by the Returns section), the description is complete. It covers purpose, parameters, return structure, and behavioral context adequately without needing to duplicate what the output schema would provide.

    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 description coverage is 0%, so the description must fully compensate. It provides excellent parameter semantics: explains what template_id represents (ID or full URL) with an example, defines limit with min/max/default values, and explains offset's role in pagination. This adds substantial meaning beyond the bare 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 verb ('Get', 'searches for', 'fetches') and resource ('template instances') with specific scope ('that belong to the input template ID'). It distinguishes from the sibling tool 'get_template' by focusing on instances rather than templates themselves. The purpose is specific and unambiguous.

    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 context ('to avoid token limit issues') and mentions pagination, but doesn't explicitly state when to use this tool versus alternatives or when not to use it. No direct comparison with the sibling 'get_template' is provided, leaving some ambiguity about tool selection.

    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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