BioOntology 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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_termsC | Search across ontology terms with advanced filtering options |
| search_propertiesC | Search ontology properties by their labels and IDs |
| search_ontologiesC | Search for ontologies by name, description, or domain |
| get_ontology_infoB | Get detailed information about a specific ontology |
| annotate_textC | Analyze text and identify relevant ontology terms with configurable parameters |
| recommend_ontologiesC | Get ontology recommendations for text or keywords with customizable weights |
| batch_annotateC | Process multiple texts for annotation efficiently |
| get_class_infoB | Get detailed information about a specific ontology class |
| get_ontology_metricsC | Get usage statistics and quality metrics for an ontology |
| get_analytics_dataC | Get visitor statistics and popularity trends with optional date filtering |
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 10 tools
Most tools have clearly distinct purposes, such as annotate_text for text analysis, get_class_info for class details, and search_ontologies for ontology discovery. However, get_analytics_data and get_ontology_metrics both involve statistics, which could cause some confusion as they overlap in tracking usage or popularity data.
All tool names follow a consistent verb_noun pattern, such as annotate_text, get_class_info, and search_ontologies. This uniformity makes it easy for agents to predict and understand the tool functions without any deviations in style.
With 10 tools, the server is well-scoped for bio-ontology tasks, covering annotation, retrieval, search, and analytics. Each tool serves a specific function, such as batch processing or recommendation, ensuring a comprehensive yet manageable set.
The tool set provides strong coverage for ontology exploration, including search, annotation, and information retrieval. A minor gap exists in update or management operations, such as modifying ontology data, but core workflows for analysis and discovery are well-supported.