Skip to main content
Glama
tonysheesh

MCP-Shield Pro

by tonysheesh
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool serves a clearly distinct purpose: mcp_scan audits for vulnerabilities, generate_aibom produces a supply chain document, and mcp_verify checks cryptographic signatures. There is no overlap or ambiguity between these operations.

    Naming Consistency4/5

    All tool names use an imperative verb followed by an object (scan, generate, verify), but the prefixes are inconsistent: two use 'mcp_' and one uses 'generate_'. This is a minor deviation from a fully uniform naming pattern.

    Tool Count5/5

    With exactly 3 tools, the server is tightly scoped to its purpose of MCP security assurance. Each tool covers a distinct, valuable function without bloat or excessive overlap.

    Completeness4/5

    The set covers the core security lifecycle: vulnerability scanning, supply chain artifact generation, and request verification. A possible gap is a remediation or compliance-check tool, but for a focused security utility, the surface is quite complete.

  • Average 3.4/5 across 3 of 3 tools scored. Lowest: 2.7/5.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior1/5

    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 'conforming to standards' but no side effects, output location, required project structure, or any other behavioral traits. This is a significant gap for a generation 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 a single concise sentence that front-loads the core action and resource. There is no redundancy or fluff, but it omits essential behavioral details, leaving it slightly under-specified for such an important tool.

    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?

    The tool has no output schema and no annotations, so the description should explain return values and side effects. It does not mention what the generated AIBOM contains, where it is written, or any prerequisites, making the tool behaviorally incomplete.

    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 documents the only parameter (projectPath) with a description, achieving 100% coverage. The tool description adds no parameter-level meaning beyond what the schema provides, so the baseline of 3 applies.

    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 identifies a specific verb ('Generate') and resource ('AI Bill of Materials'), and adds a compliance context ('conforming to AI supply chain governance standards'). However, it does not explicitly distinguish this tool from siblings mcp_scan and mcp_verify, so it misses full 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?

    The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It simply states what the tool does, leaving the agent without decision context relative to sibling tools.

    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 provided, so the description bears the full responsibility for disclosing side effects or behavior. It does not mention whether the verification returns a boolean, raises errors, or has any security implications, leaving the agent uninformed about expected outcomes.

    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 entire description is a single, contained sentence that states the action without redundancy. It is front-loaded and every word contributes meaning.

    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 only one nested parameter and no output schema, the description is too sparse. It lacks any mention of return values, failure modes, or how the signed payload should be structured beyond the schema, making it insufficient for an agent to confidently invoke the tool.

    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 includes a description for the sole 'signedPayload' parameter ('Signed payload containing toolName, parameters, and meta'), giving 100% schema coverage. The tool description itself adds no further parameter semantics, so the baseline of 3 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?

    The description uses the specific verb 'verify' with a clear resource ('MCP tool call request signature'), which immediately distinguishes it from siblings like mcp_scan and generate_aibom. The cryptographic nature is also stated, leaving no ambiguity about the tool's core function.

    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 about when to use this tool versus alternatives, nor any mention of prerequisites or contexts where verification is needed. The description simply states the action without helping the agent decide when to invoke it.

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

  • Behavior3/5

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

    With no annotations, the description must carry the full burden. It conveys that the tool performs an audit (implying non-destructive analysis) and takes a local path, but it doesn't explicitly state whether it is read-only, what side effects it might have, or what output to expect.

    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 is concise and information-dense. Every word contributes to understanding the tool's purpose and target.

    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?

    The tool is simple with one well-documented parameter and no output schema. The description sufficiently covers the main purpose and target types. It could mention return format or additional behavior, but given the simplicity, it is largely complete.

    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 schema covers the single 'path' parameter with 100% coverage, but the description adds meaning by specifying the target types: 'MCP server, codebase, or configuration file.' This goes beyond the schema's generic 'File or directory path to audit.'

    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 function: 'Audit an MCP server, codebase, or configuration file against OWASP MCP Top 10 vulnerabilities.' It uses a specific verb (audit), identifies the resource types, and specifies the security standard, which distinguishes it from sibling tools like generate_aibom and mcp_verify.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage when an audit against OWASP MCP Top 10 is needed, providing clear context. However, it does not explicitly mention alternatives or exclusion criteria, so it stops short of the highest score.

    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

mcp-shield-pro MCP server

Copy to your README.md:

Score Badge

mcp-shield-pro 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/tonysheesh/mcp-shield-pro'

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