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liangjunyu2010

Baidu Cloud AI Content Safety MCP Server

by liangjunyu2010
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose: analyzing text for safety risks, which cannot be confused with any other tool in this set.

    Naming Consistency5/5

    The naming is trivially consistent as there is only one tool. The tool name 'input_analyze' follows a clear verb_noun pattern, and with no other tools to compare, it cannot exhibit any inconsistency.

    Tool Count2/5

    A single tool is too few for a server with the broad purpose of 'Baidu Cloud AI Content Safety,' which suggests capabilities like image analysis, video moderation, or multiple text checks (e.g., spam, hate speech). This minimal set severely limits the server's utility and scope.

    Completeness2/5

    The server is severely incomplete for content safety. It only handles text input analysis, missing obvious gaps like image or video safety checks, batch processing, or different risk categories (e.g., political, adult). This will cause agent failures when non-text content needs moderation.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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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 carries the full burden of behavioral disclosure. It mentions detecting risky content but doesn't specify what types of risks (e.g., hate speech, spam), how results are returned, error conditions, or any rate limits. 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 with the tool's name and purpose. It uses a clear structure with Name, Description, and Args sections, though the formatting could be more polished. Every sentence earns its place without unnecessary elaboration.

    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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., risk score, categories), error handling, or operational constraints. For a tool with behavioral complexity (risk detection), this leaves too many unknowns for an agent.

    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 0%, so the description must compensate. It adds meaning by specifying 'text' as '待检测文本' (text to be detected), which clarifies the parameter's purpose. However, it doesn't provide details on text length limits, language support, or format requirements, leaving some ambiguity.

    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 tool's purpose: '检测文本安全' (detect text safety) and '将文本进行检测，判断是否存在风险内容' (analyze text to determine if it contains risky content). It specifies the verb (detect/analyze) and resource (text), though it doesn't differentiate from siblings since none exist.

    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 versus alternatives. The description only states what the tool does, not when it should be invoked or any prerequisites. With no sibling tools, this is less critical, but still a gap in usage context.

    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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