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SoulChildTc

ai-bridge

by SoulChildTc
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: check_connection verifies connectivity, new_session initiates a new conversation, and ask_ai continues an existing one. No functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern (check_connection, new_session, ask_ai), maintaining clear and predictable naming.

    Tool Count5/5

    Three tools are perfectly scoped for the AI bridge server's purpose: connectivity check, session creation, and conversation continuation. No bloat or deficiency.

    Completeness4/5

    The tool set covers the core workflow (connect, create session, converse), but lacks session management tools like closing or listing sessions, which is a minor gap.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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.

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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.

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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 bears full responsibility. It only states the tool checks connection status and implies that after connection, other tools work. It does not describe return format, error behavior, or whether it establishes or just checks the connection, leaving significant ambiguity.

    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 consists of two clear, front-loaded sentences. Every sentence adds value: purpose and usage guidance. No superfluous content.

    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?

    Given the low complexity (no parameters, no output schema, simple check), the description is mostly adequate but misses behavioral details like return type or state changes. It would benefit from specifying the output (e.g., boolean for connected/not connected).

    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 tool has zero parameters, and the schema covers 100%. Per instructions, baseline is 4. The description adds no parameter information, which is acceptable as there are none to document.

    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 checks connection status with the browser. It distinguishes itself from sibling tools 'new_session' and 'ask_ai' by recommending usage before them, indicating it is a preparatory diagnostic tool.

    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 explicitly recommends using this tool before first calling 'new_session' or 'ask_ai', giving clear context. It does not mention when not to use or alternatives, but the recommendation is specific enough.

    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?

    No annotations are provided, so the description must disclose behavioral traits. It mentions ensuring messages go to the correct conversation, but it does not describe any side effects, authentication requirements, rate limits, or whether the tool modifies state. For a continuation tool, this is minimally transparent but lacks depth.

    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, well-structured sentence in Chinese. It is concise with no redundancy, front-loading the core purpose. Every word earns its place, and the instruction is immediately actionable.

    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 the tool's simplicity (3 required parameters, no output schema, no nested objects), the description is fairly complete. It explains the necessary preconditions (new_session provides sessionUrl and platform) and the action. However, it does not describe the return value or behavior when the conversation continues (e.g., response format). The sibling tools help provide broader 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?

    Schema description coverage is 100%, meaning input schema already provides descriptions for all three parameters. The tool description adds context about the purpose of sessionUrl and platform (from new_session), but does not provide additional meaning beyond what is in the schema. Baseline score of 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 continues communication in an existing consultation conversation. It references 'new_session' to establish context, distinguishing it from sibling tools 'new_session' (creates session) and 'check_connection' (probably tests connectivity). The verb 'continue' and resource 'conversation' are specific and unambiguous.

    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 explicitly instructs to pass the sessionUrl and platform from new_session, providing clear when-to-use guidance. It implies that the tool should only be used after new_session, but it does not explicitly mention when not to use this tool or suggest alternatives. The context is clear but could be more comprehensive with exclusion criteria.

    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?

    No annotations provided, so description carries full burden. It covers return values, security (no sensitive info), and session reuse. However, it lacks details on error handling, rate limits, or state changes beyond creation.

    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?

    Concise single paragraph, front-loaded with main purpose, then specific guidelines. Every sentence adds value with no redundancy.

    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?

    No output schema, but description mentions essential return values (sessionUrl, platform). For a session creation tool, it is fairly complete, though could detail response structure and error cases.

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

    Parameters5/5

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

    Schema coverage is 100%, and description adds value by explaining what to include in message (problem, attempts, blockers, code) and platform options. Also warns against including sensitive info.

    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 explicitly states the tool initiates a new consultation session with Web AI, mentions return values (sessionUrl, platform), and clearly distinguishes from ask_ai by specifying when to use each.

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

    Usage Guidelines5/5

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

    Provides specific conditions for use (no sessionUrl in context or user requests new session) and advises preferring ask_ai otherwise, effectively differentiating from 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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