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liueic

PubChem Chemical Safety MCP Server

by liueic

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: get_compound_info retrieves basic chemical properties, get_safety_info focuses on GHS hazard classifications, and get_toxicity_data provides experimental toxicity data. There is no overlap in functionality, and the descriptions clearly differentiate their domains (basic info vs. safety vs. toxicity).

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with 'get_' prefix and snake_case naming (get_compound_info, get_safety_info, get_toxicity_data). This predictable structure makes it easy for agents to understand and navigate the toolset without confusion.

    Tool Count3/5

    With only 3 tools, the set feels thin for a chemical safety domain that might benefit from additional operations like searching compounds, batch queries, or regulatory data. While the tools cover core areas, the low count limits comprehensive coverage and could require agents to work around missing functionality.

    Completeness3/5

    The tools cover key aspects (basic info, safety, toxicity) but leave notable gaps. There is no search or lookup tool to find compounds by criteria, no batch operations for efficiency, and no update/delete capabilities (though less critical here). Agents may struggle to initiate workflows without a way to discover compounds beyond direct name input.

  • 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
    • 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 MIT License.

  • 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 full burden. It mentions returning a dictionary with CID, molecular formula, and molecular weight, which adds some behavioral context about output format. However, it lacks details on permissions, rate limits, error handling, or whether it's a read-only operation, which is insufficient for a tool with no annotation coverage.

    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 concise with a clear purpose statement followed by Args and Returns sections. However, the Args section is redundant with the input schema (repeating 'name' without added value), and the structure could be more front-loaded by integrating parameter hints into the main description.

    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 tool has an output schema (which should cover return values), the description's mention of return format is somewhat redundant. It provides minimal context for a simple lookup tool but lacks guidance on usage versus siblings and behavioral details, making it incomplete despite the output schema support.

    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 that the 'name' parameter is a compound name, providing basic semantics beyond the schema's generic 'Name' title. However, it doesn't specify format constraints (e.g., IUPAC name, common name) or examples, leaving gaps in parameter understanding.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool '获取化合物基础信息' (get compound basic information), which is a clear verb+resource combination. However, it doesn't differentiate from sibling tools like get_safety_info or get_toxicity_data, leaving the scope ambiguous about what distinguishes 'basic information' from safety or toxicity data.

    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 like get_safety_info or get_toxicity_data. The description implies it's for basic compound information but doesn't specify use cases, prerequisites, or exclusions, leaving the agent to guess based on tool names 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format ('包含急性毒性、生态毒性、致癌性等毒性数据的字典' - dictionary containing acute toxicity, ecotoxicity, carcinogenicity, etc.) but doesn't address important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or data freshness. For a data retrieval tool with zero annotation coverage, this leaves significant gaps.

    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 concise with three clear sections: purpose statement, parameter documentation, and return value description. Each sentence earns its place by providing essential information. The structure with labeled 'Args:' and 'Returns:' sections is helpful, though the formatting could be more consistent.

    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 tool has an output schema (which presumably documents the return structure), the description doesn't need to fully explain return values. However, for a toxicity data retrieval tool with no annotations and only basic parameter documentation, the description should provide more context about data sources, limitations, or typical use cases. It's minimally adequate but leaves important questions unanswered.

    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 description explicitly documents the single parameter 'cid' as 'PubChem化合物ID' (PubChem compound ID), which adds crucial semantic meaning beyond the schema's generic 'Cid' title and integer type. With 0% schema description coverage, this parameter documentation is essential. However, it doesn't provide format examples, valid ranges, or handling of invalid inputs.

    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 as '获取毒性实验数据' (get toxicity experiment data), which is a specific verb+resource combination. It distinguishes from sibling tools like 'get_compound_info' and 'get_safety_info' by focusing specifically on toxicity data. However, it doesn't explicitly differentiate itself from 'get_safety_info' which might overlap in scope.

    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 like 'get_safety_info'. There's no mention of prerequisites, appropriate contexts, or exclusions. The only implied usage is when toxicity data for a specific compound is needed, but this is too vague for effective tool selection.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a retrieval operation ('获取' - get) which implies read-only behavior, but doesn't explicitly confirm this or mention any side effects, authentication requirements, rate limits, or error conditions. The description provides basic functional information but lacks important behavioral context for a tool with zero annotation coverage.

    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 concise with clear sectioning: a purpose statement followed by Args and Returns sections. Each sentence serves a specific purpose - stating what the tool does, explaining the parameter, and describing the return value. There's no redundant information, though the structure could be slightly more polished.

    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 tool has an output schema (which handles return value documentation) and only one parameter, the description provides adequate context for the basic operation. However, with no annotations and sibling tools that appear related, the description should ideally provide more guidance on when to use this specific tool versus alternatives. It covers the essentials but leaves gaps in usage context.

    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 description adds significant value beyond the input schema, which has 0% description coverage. It explains that 'cid' represents a 'PubChem化合物ID' (PubChem compound ID), providing crucial semantic context that the schema lacks. With only one parameter and the schema providing no descriptions, the tool description effectively compensates for the schema's deficiencies by explaining what the parameter represents.

    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: '获取GHS安全分类信息' (Get GHS safety classification information). It specifies the resource (GHS safety classification) and the action (get/retrieve). However, it doesn't explicitly differentiate from sibling tools like 'get_compound_info' or 'get_toxicity_data' - all appear to retrieve chemical information but for different aspects.

    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 the sibling tools. While it mentions the specific parameter (PubChem compound ID), it doesn't explain when one would need safety information versus general compound information or toxicity data. There's no context about prerequisites, alternatives, or exclusion criteria.

    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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