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Glama
OHNLP

OMOP MCP Server

by OHNLP
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OMOPHUB_API_KEYYesAPI key for OMOP HUB vocabulary lookups (obtain from omophub.com)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
find_omop_conceptB
Find OMOP concepts for a given keyword, table, and field.
Returns multiple candidates for LLM to choose from based on context.

Args:
    keyword: The clinical term to map
    omop_table: The OMOP CDM table name
    omop_field: The concept ID field name
    max_results: Maximum number of candidate concepts to return

Returns:
    Dict containing candidate concepts or error information if no results found.
batch_map_concepts_from_csvC

Process a CSV file of keywords, mapping each row and returning a CSV with mapping results appended as new columns.

Prompts

Interactive templates invoked by user choice

NameDescription
map_clinical_conceptCreate a prompt for mapping clinical concepts.

Resources

Contextual data attached and managed by the client

NameDescription
omop://tables
omop://documentation
omop://preferred_vocabularies
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.3/5.0

Scored across 2 tools

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.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources