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bhawnapannu2701

workbench-mcp

by bhawnapannu2701
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: listing files, reading a file, and returning workspace information. No ambiguity or overlap.

    Naming Consistency5/5

    All tools follow a consistent verb_noun snake_case pattern (list_files, read_file, workspace_info), making them predictable and easy to understand.

    Tool Count4/5

    Three tools is minimal but reasonable for a focused workspace file server. The count feels slightly low but not problematic given the apparent scope.

    Completeness2/5

    The set covers listing and reading files but lacks write, delete, or directory operations, leaving significant gaps for typical file management workflows.

  • Average 2.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 10 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.

  • 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 bears full responsibility for disclosing behavioral traits. It only states 'list files', omitting whether the operation is read-only, whether it requires permissions, the scope depth, or any side effects. The presence of an output schema is not leveraged to clarify return behavior.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    The description is a single, short sentence with no wasted words. However, it is under-specified for a tool with 4 parameters and no schema descriptions, making conciseness come at the cost of completeness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

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

    Given the tool has 4 parameters, no schema descriptions, and an output schema, the description is critically incomplete. It fails to explain parameter behavior, return values, or usage context, making it inadequate for effective agent invocation.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description adds no meaning to any of the 4 parameters (pattern, max_depth, max_results, relative_directory). It fails to clarify their roles, defaults, or constraints, leaving the agent to rely solely on the schema's field names and types.

    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 'list' and resource 'files', and specifies the scope 'workspace-contained directory', distinguishing it from siblings like 'read_file' (reads a single file) and 'workspace_info' (workspace-level info). However, 'workspace-contained directory' is slightly vague, lacking specificity about whether it lists recursively or the root 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?

    No guidance is given on when to use this tool versus its siblings (read_file, workspace_info). The description does not mention prerequisites, context, or alternatives, leaving the agent to infer usage from the name alone.

    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?

    No annotations are present. The description does not disclose behavioral traits such as error handling (e.g., file not found), permissions required, or that reading may be restricted. It merely states the basic function without beyond what is obvious.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    The description is a single concise sentence with no fluff. However, it is overly brief, sacrificing essential information for brevity. It earns its place but could be more informative.

    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 presence of an output schema and sibling tools, the description is incomplete. It fails to explain the partial reading feature, return format, or any edge cases. The minimal description does not cover the complexity of three parameters.

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

    Parameters1/5

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

    The schema has 0% coverage with no parameter descriptions. The tool description does not explain any of the three parameters (relative_path, start_line, line_count), leaving the agent without guidance on how to use them meaningfully.

    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 (Read) and resource (a workspace-contained UTF-8 text file). It specifies encoding and scope, distinguishing it from siblings like list_files (which lists files) and workspace_info (which provides workspace metadata), though it does not explicitly differentiate.

    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 vs alternatives, or any prerequisites. The description lacks context for appropriate usage, leaving the agent to infer from the name alone.

    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?

    No annotations provided; description only says 'sanitized' without explaining what that entails, and lacks disclosure of auth needs or rate 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?

    Single sentence, no extraneous words, efficiently communicates purpose.

    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?

    With output schema covering return values, description is mostly complete, though 'sanitized' could be elaborated.

    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?

    No parameters exist (100% schema coverage), so description need not add param info; baseline 4 applies.

    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?

    Description clearly states the tool returns workspace and server capability information, distinguishing it from sibling tools that handle file operations.

    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?

    Implicitly useful for getting workspace info, but no explicit guidance on when to use or alternatives beyond sibling tool names.

    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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