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OHNLP

OMOP MCP Server

by OHNLP
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Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one handles single keyword lookup, the other processes CSV files in batch. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case pattern with descriptive verbs (find, batch_map) and nouns (concept, concepts from CSV), making them predictable.

    Tool Count3/5

    With only 2 tools, the set feels thin even for a narrow domain. While it covers the core mapping function, a few more tools (e.g., get concept details) would make it more robust.

    Completeness3/5

    The tools cover single and batch mapping, which are the primary use cases. However, missing operations like retrieving concept details or filtering by domain leave minor gaps for advanced workflows.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • 0 of 3 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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.

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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 burden but only states the high-level action. It fails to disclose side effects (e.g., whether the input file is modified or deleted), resource requirements, error handling, or idempotency. Critical behavioral traits are missing.

    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-formed sentence with no redundancy. It is front-loaded with the verb 'Process' and immediately identifies the resource. Every word contributes to understanding.

    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 tool has one parameter, no annotations, and no output schema. The description does not specify the output CSV format, mapping logic (e.g., what concepts are mapped to), or error behavior. For a batch processing tool, this is insufficient for safe autonomous invocation.

    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 single parameter csv_path is explained implicitly: 'Process a CSV file of keywords' clarifies it's a file path for a keyword CSV. However, with 0% schema description coverage, more detail is expected (e.g., allowed formats, absolute vs relative paths). The description adds minimal value beyond the schema itself.

    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 processes a CSV of keywords, maps each row, and returns a CSV with appended results. It implies batch operation, distinguishing it from the sibling find_omop_concept (single concept lookup). However, it doesn't explicitly name the mapping target (e.g., OMOP concepts), slightly reducing precision.

    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 find_omop_concept. There is no mention of prerequisites, ideal scenarios, or cases where this tool should be avoided. The agent receives no decision support.

    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 carries the burden of behavioral disclosure. It states the tool returns candidate concepts or error information, but does not mention side effects, authentication needs, rate limits, or pagination behavior. It is adequate but not thorough for a lookup tool.

    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 relatively short but includes redundant bullet-like formatting and whitespace. It could be more concise by combining the argument list into a sentence. The structure is acceptable but not 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 no output schema, the description explains the return format as a dict with candidate concepts or error info. However, it lacks details on the structure of each candidate, pagination, or how to interpret results. For a tool that returns multiple candidates, more context on the output would improve completeness.

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

    Parameters2/5

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

    The input schema has 0% parameter description coverage. The description lists parameter names (keyword, omop_table, omop_field, max_results) but does not add meaningful context beyond the names, such as valid table/field values, format examples, or the purpose of max_results. This is insufficient for a tool with four 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 finds OMOP concepts for a given keyword, table, and field. It specifies the action (Find), resource (OMOP concepts), and context (keyword, table, field). The sibling tool 'batch_map_concepts_from_csv' implies a batch/CSV input, so this tool is distinct as a single lookup.

    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 does not explicitly state when to use this tool versus alternatives. It mentions returning multiple candidates for the LLM to choose, which hints at a decision-making use case, but lacks guidance on when not to use it or how it compares to the sibling tool (batch_map_concepts_from_csv).

    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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