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XD3an

MCP Builder

by XD3an
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

58%
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: one installs from a local directory, while the other installs from a package repository via pip or npm. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the installation source.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'install' as the verb and descriptive nouns ('local_mcp_server', 'repo_mcp_server'). The naming is predictable and readable, with no deviations in style or convention.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'Builder' purpose, which might imply broader capabilities like configuration, management, or testing of MCP servers. The count is too low for the apparent scope, limiting functionality and potentially causing gaps in agent workflows.

    Completeness2/5

    The tool surface is severely incomplete for a 'Builder' domain, as it only covers installation from two sources. Missing are obvious operations like uninstalling, listing installed servers, configuring servers, or building/testing MCP projects, which are essential for comprehensive server management.

  • Average 2.9/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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

  • Behavior2/5

    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 installing from a local directory but fails to describe critical behaviors like whether this is a destructive operation, what permissions are needed, how errors are handled, or what the expected outcome is. This leaves significant gaps for an agent to understand 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately sized and front-loaded, starting with a clear purpose statement followed by a structured parameter list. It avoids unnecessary verbosity, though the parameter descriptions could be slightly more informative without sacrificing conciseness.

    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 complexity of installing an MCP server, no annotations, no output schema, and minimal parameter details, the description is incomplete. It doesn't cover expected outputs, error conditions, dependencies, or behavioral nuances, making it inadequate for an agent to use the tool confidently in varied contexts.

    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 description includes an 'Args' section that lists and briefly describes the three parameters (path, args, env), adding meaning beyond the input schema, which has 0% description coverage. However, the explanations are minimal (e.g., 'The arguments to pass along' without specifying format or examples), providing only basic semantic value without fully compensating for the schema's lack of detail.

    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 action ('Install') and resource ('an MCP server from a local directory'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling tool 'install_repo_mcp_server', which likely installs from a repository rather than a local directory.

    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 provides no guidance on when to use this tool versus alternatives, such as the sibling tool 'install_repo_mcp_server'. It lacks context about prerequisites, typical use cases, or any exclusions, leaving the agent without clear 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 carries full burden but provides minimal behavioral context. It mentions installation but doesn't disclose important traits like whether this requires admin privileges, what happens if installation fails, whether it modifies system state permanently, or what the expected output is. The description doesn't contradict annotations since none exist.

    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 concise with a clear purpose statement followed by parameter explanations. The structure is front-loaded with the main functionality. However, the parameter explanations could be more efficiently integrated rather than listed as separate bullet points.

    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?

    For a tool that performs system installation with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't cover important context like prerequisites, error handling, success criteria, or what happens after installation. The agent lacks critical information to use this tool effectively.

    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?

    With 0% schema description coverage, the description adds value by explaining all three parameters: 'name' as package name, 'args' as arguments to pass, and 'env' as environment variables. However, it doesn't provide format details (e.g., how environment variables should be formatted with '=' delimiter), examples, or constraints beyond basic semantics.

    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 'install' and resource 'MCP server', specifying installation via pip or npm. It distinguishes from the sibling tool 'install_local_mcp_server' by implying this is for repository-based installation rather than local, though not explicitly stated.

    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 explicit guidance on when to use this tool versus alternatives is provided. The existence of sibling tool 'install_local_mcp_server' suggests there are different installation methods, but the description doesn't explain when to choose repository vs local installation.

    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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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.

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