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biancaroh-design

entity-extraction-mcp

by biancaroh-design
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TypeScript
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools, recommend_coupons and issue_ticket, have completely distinct purposes: one handles coupon recommendations based on extracted entities, while the other issues tickets for delivery delays. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun pattern: recommend_coupons and issue_ticket. This makes the naming predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server is on the low end of the scale. While the tools themselves are focused, the server's name suggests a broader entity-extraction purpose, making the count feel somewhat thin.

    Completeness1/5

    The server is named entity-extraction-mcp, but there are no tools for extracting entities at all. The available tools are downstream actions that rely on extracted data, creating a severe gap between the server's stated purpose and its actual capabilities.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

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  • 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?

    With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only states the action and a required parameter, but does not mention whether the operation is read-only, has side effects, requires auth, or what happens if required data is missing. This lack of context for a tool with no annotations is a significant gap.

    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 extremely concise: two sentences that state the purpose and the key requirement with no filler. It is front-loaded and every word earns its place, making it highly efficient.

    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's simplicity (3 simple string-array parameters, no output schema), the description is minimally adequate. It explains what the tool does and the required parameter. However, since there is no output schema, the description does not explain what the tool returns (e.g., a list of recommended coupons), leaving a gap for the agent to infer.

    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 all three parameters (times, places, activities). The description adds no semantic value beyond noting that the 'places' array is required, which is already present in the schema. This meets the baseline but does not exceed it.

    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 function: recommending membership coupons based on extracted entities. It uses a specific verb ('recommends') and resource ('membership coupons'), making the purpose clear. However, it does not explicitly differentiate from the sibling tool 'issue_ticket', so it doesn't fully earn a 5.

    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 clear context for when to use the tool: when you have extracted entities and need coupon recommendations, with the explicit prerequisite that the 'places' array is required. It does not mention alternatives or exclusions, but the context is sufficiently clear for basic usage.

    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 burden of behavioral disclosure. It only states the action (issuing a ticket) without mentioning side effects, permissions, idempotency, or response behavior. It does not go beyond the obvious creation aspect.

    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 concise sentence in Korean that immediately states the tool's purpose. It is front-loaded and contains no filler, making it highly efficient.

    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?

    The description is minimal and lacks information about expected return values or post-issuance behavior, especially given there is no output schema. It also does not integrate with the sibling tool context. The schema covers parameters, but the overall description does not provide enough operational context for a complete understanding.

    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 input schema includes descriptions for all six parameters, so schema description coverage is 100%. The tool description adds no additional parameter meaning, leaving the schema to fully explain each field. This meets the baseline for schema-covered parameters.

    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 issues tickets specifically for delivery delay cases (배송 지연 건에 대한 티켓을 발급합니다), using a specific verb '발급' and resource '티켓'. This distinguishes it from the sibling tool recommend_coupons, which handles coupon recommendations, so the purpose is 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 provides a clear context (orders with delivery delays) but does not explicitly state when not to use it or mention alternatives like recommend_coupons. It implies usage for delay scenarios but lacks explicit exclusion or comparison.

    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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  • 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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