Omics AI MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_collectionsC | List all collections available in an Omics AI Explorer network |
| list_tablesC | List all tables in a specific collection |
| get_schema_fieldsC | Get the schema fields for a specific table |
| query_tableC | Query data from a table with optional filters and pagination |
| count_rowsC | Count the number of rows matching given filters |
| sql_searchC | Execute a SQL query against a collection using Trino syntax |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: counting rows, retrieving schema fields, listing collections, listing tables, querying tables, and executing SQL queries. The descriptions clearly differentiate their functions, making misselection unlikely.
All tools follow a consistent verb_noun pattern (e.g., count_rows, get_schema_fields, list_collections, list_tables, query_table, sql_search). The naming is uniform and predictable throughout the set.
With 6 tools, the set is well-scoped for an Omics AI data exploration server. Each tool serves a specific and necessary function, covering core operations without being overly sparse or bloated.
The toolset provides comprehensive coverage for data querying and exploration, including listing, querying, and schema inspection. A minor gap exists in data manipulation (e.g., insert/update/delete operations), but the core workflows for analytics are well-supported.