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teehooai

teeshield

by teehooai
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.3.5

  • Disambiguation5/5

    The two tools target completely different security domains—agent installations vs MCP servers—with no functional overlap. An agent can easily distinguish which to use based on the target.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: check_agent_security and scan_mcp_server. The naming is predictable and immediately conveys purpose.

    Tool Count4/5

    With only two tools, the server is minimal but well-scoped to its purpose of security scanning. Each tool covers a distinct area, and adding more would risk bloat. However, a third tool for scanning server configurations could be justified.

    Completeness4/5

    The set covers the two primary use cases implied by the server name 'teeshield': scanning agents and scanning MCP servers. Agent scanning includes configuration and skills; MCP scanning includes vulnerabilities and description quality. Minor gaps like scanning for network security or runtime behavior might exist, but are outside the stated scope.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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

  • Behavior4/5

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

    Without annotations, the description adequately discloses the tool's behavior: it scans configuration and skills, and returns findings with severity and fix hints. It implies a read-only operation, though explicitly stating non-destructiveness would improve 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 concise: two sentences for the main functionality and one for usage. Every sentence provides value, no redundancy, and the structure is front-loaded with the core 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?

    Given no output schema, the description covers the return format (findings with severity and fix hints). It adequately describes the tool's scope and outcome, though it could mention the sibling tool to avoid confusion.

    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?

    Schema description coverage is 100%, so the schema already documents each parameter well. The description adds overall context but does not enhance parameter semantics beyond what the schema provides, meeting the baseline.

    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 'scan' and the resource 'AI agent installation'. It lists specific security checks on configuration and skills, distinguishing it from the sibling tool 'scan_mcp_server' which likely scans MCP servers instead.

    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?

    Explicit usage guidance is provided: 'Use when auditing an agent's security posture or before deploying an agent to production.' This gives clear context but does not mention when not to use or alternative tools, such as the sibling for MCP servers.

    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, the description carries full burden. It explains the tool scans for specific vulnerabilities, scores descriptions, and returns a rating. It does not disclose whether it modifies files or requires network access, but overall it provides substantial behavioral insight.

    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 concise, with every sentence serving a purpose. It starts with the main action, lists what it checks, and ends with when to use it. No wasted words.

    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 simple one-parameter schema, no output schema, and no annotations, the description is complete. It explains the tool's purpose, checks, and output (security rating with recommendations). There are no apparent gaps.

    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 single parameter 'target' is described in the schema as 'GitHub repo URL or local directory path'. The tool description does not add new meaning beyond that, so a baseline score of 3 is appropriate given 100% schema coverage.

    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 scans MCP servers for security vulnerabilities, description quality, and architecture issues, listing specific checks and a rating system. It distinguishes itself from the sibling tool 'check_agent_security' by focusing on server security rather than agent security.

    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 provides a clear use case: 'Use when evaluating whether an MCP server is safe to install or deploy.' However, it does not mention when not to use this tool or contrast it with alternatives like the sibling tool.

    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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