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Tangyufei233

pharma-assistant-mcp

by Tangyufei233
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one initiates a research process and returns its status, the other retrieves evidence metadata. No overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern in snake_case (run_research_intelligence, get_finding_evidence), which is clear and predictable.

    Tool Count2/5

    With only 2 tools for a pharmaceutical assistant, the tool surface is extremely thin. A typical assistant for this domain would require many more tools for tasks like drug lookup, adverse event reporting, and clinical trial management.

    Completeness2/5

    The tool set covers only initiating research and viewing evidence metadata, missing essential operations such as creating/updating/deleting findings, searching existing data, or managing workflows. The surface feels incomplete for the stated domain.

  • Average 2.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • 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 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?

    With no annotations, the description bears full responsibility for behavioral disclosure. It states one behavioral fact (no local file path) but omits details such as whether the operation is read-only, idempotent, or requires authentication. The single sentence provides minimal transparency.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, which is concise. However, it is too brief and lacks necessary detail, making it less effective than a more structured yet concise description.

    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 simplicity (1 required parameter) and the existence of an output schema, the description still fails to provide adequate context. It does not mention output structure, usage prerequisites, or how it relates to the sibling tool. It is 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.

    Parameters1/5

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

    The description does not explain the 'finding_id' parameter at all. With 0% schema description coverage, the parameter is completely opaque. The description adds no semantic value beyond what the schema's property name implies.

    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 action ('查看证据元数据' - view evidence metadata) and adds an important exclusion (does not return local file path). While it distinguishes itself from the sibling tool by implication, it does not explicitly mention the difference.

    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 the sibling tool 'run_research_intelligence' or in what scenarios it is appropriate. The description lacks usage context.

    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, the description must bear full weight. It states the tool returns 'running status for review', implying an asynchronous operation, but does not clarify side effects, permissions, or whether results are stored. No behavioral traits beyond the literal action are disclosed.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence with no fluff, making it concise. However, it is so terse that it omits necessary details, crossing from conciseness into under-specification.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has 3 parameters, no output schema, and no annotations, the description is severely incomplete. It fails to explain return values, parameter usage, or process details, making it inadequate for proper tool selection and invocation.

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

    Parameters1/5

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

    The input schema has three parameters with no descriptions, and the tool description provides no additional meaning. The agent receives no help understanding 'topic', 'preset', or 'max_results', despite schema coverage being 0%. This is a critical gap.

    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 verb 'run' and the resource 'research intelligence', indicating it initiates a process. It is differentiated from the sibling tool 'get_finding_evidence' by focusing on running rather than retrieving. However, it does not specify what 'research intelligence' entails, leaving the purpose somewhat vague.

    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. There is no mention of prerequisites, context, or exclusions. The description only states what the tool does without any usage recommendations.

    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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