Skip to main content
Glama
Conceptual-Machines

tracktion-engine-mcp

by Conceptual-Machines
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Search and get-docs are clearly distinct operations: one discovers classes via query, the other retrieves detailed documentation for a specific class. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with the 'te' prefix (search_te_classes, get_te_class_docs), making the naming uniform and predictable.

    Tool Count3/5

    Two tools is minimal but defensible for a focused documentation lookup server. It feels slightly thin, but the pair covers the essential search-and-retrieve workflow.

    Completeness4/5

    The search and get-docs pair forms a complete workflow for browsing class documentation. No critical operations like listing or cross-referencing are obviously required for the stated purpose.

  • Average 3.2/5 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 status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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 does not explain what the search returns, whether matching is partial/fuzzy, case sensitivity, or any other behavioral traits. This leaves the agent with insufficient information about the tool's behavior.

    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 efficiently front-loads the action and resource. It is appropriately sized with no unnecessary words or redundant information, demonstrating excellent conciseness and structure.

    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?

    With no output schema and no annotations, the description should at least indicate the return format or result behavior. It does neither, leaving the agent without knowledge of what the search yields or how results are presented. This makes the tool contextual incomplete despite its simplicity.

    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 ('query' and 'limit') are fully documented in the schema with descriptions, giving 100% schema coverage. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

    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 uses the specific verb 'search' and identifies the resource as 'Tracktion Engine classes,' with a clear scope of matching by name or description. It does not explicitly contrast with the sibling 'get_te_class_docs,' but the purpose is unambiguous and distinct enough for a 4.

    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?

    There is no guidance on when to use this tool versus the sibling 'get_te_class_docs,' nor any mention of typical workflows, prerequisites, or exclusions. The description simply states what the tool does without any usage direction.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It only says 'get detailed documentation', which is nearly as vague as the tool name. It does not describe the return format, error handling for unknown classes, or any potential side effects (though none are likely). This is insufficient transparency.

    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, front-loaded sentence that directly conveys the tool's purpose without superfluous words. It is appropriately concise for a simple tool.

    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 tool with only one parameter and no output schema, the description is minimally adequate but leaves gaps. It does not clarify what 'detailed documentation' entails (e.g., text format, fields), and it fails to explicitly differentiate from the sibling search tool. The simplicity of the tool partially compensates for this missing context.

    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 input schema already provides a clear description for 'className' with examples ('Edit', 'AudioTrack', 'Plugin'), and coverage is 100%. The tool description adds the 'Tracktion Engine' context but does not materially enhance parameter understanding beyond what the schema already does, 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 the tool's verb and resource: 'Get detailed documentation for a specific Tracktion Engine class'. It distinguishes itself from the sibling 'search_te_classes' by focusing on a specific class rather than search, making the purpose unmistakable.

    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 that the user must already know the exact class name ('specific class'), but it does not explicitly mention when to use this tool over the sibling 'search_te_classes'. There is no guidance on what to do if the class is unknown or if the tool can handle non-existent class names.

    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

tracktion-engine-mcp MCP server

Copy to your README.md:

Score Badge

tracktion-engine-mcp MCP server

Copy to your README.md:

Latest Blog Posts

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/Conceptual-Machines/tracktion-engine-mcp'

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