Used for version control, contribution, and distribution of the Gene Ontology MCP server codebase.
Used for installation and package management of the Gene Ontology MCP server.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GeneOntology MCP Server search for apoptosis-related biological processes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A comprehensive Model Context Protocol server for accessing Gene Ontology (GO) data, enabling AI systems to perform ontology-based analysis, gene annotation research, and functional enrichment studies.
Term Search & Lookup: Search GO terms by keyword, name, or definition
Ontology Navigation: Explore hierarchical relationships between terms
Gene Annotations: Access GO annotations for genes and proteins
Validation: Validate GO identifiers and check term existence
Statistics: Get comprehensive ontology statistics and metadata
Related MCP server: OWL-MCP
npm install
npm run buildAdd to your MCP client configuration:
{
"mcpServers": {
"go-server": {
"command": "node",
"args": ["/path/to/go-server/build/index.js"]
}
}
}Search across Gene Ontology terms by keyword, name, or definition.
{
"query": "apoptosis",
"ontology": "biological_process",
"size": 10
}Get detailed information for a specific GO term.
{
"id": "GO:0006915"
}Validate GO identifier format and check if term exists.
{
"id": "GO:0008150"
}Get statistics about GO ontologies (term counts, recent updates).
{
"ontology": "all"
}go://term/{id} - Complete GO term information
go://annotations/{gene} - Gene annotations
go://search/{query} - Search results
go://hierarchy/{id} - Term hierarchy relationships
# Using the search_go_terms tool
{
"query": "apoptosis",
"ontology": "biological_process",
"size": 5,
"include_obsolete": false
}# Using the get_go_term tool
{
"id": "GO:0006915"
}# Using the validate_go_id tool
{
"id": "GO:0008150"
}This server integrates with:
QuickGO API (https://www.ebi.ac.uk/QuickGO/) - Primary data source
GO Consortium (https://geneontology.org/) - Official Gene Ontology
AmiGO (http://amigo.geneontology.org/) - Additional browsing interface
The server includes comprehensive error handling for:
Invalid GO identifiers
Missing terms
API timeouts
Network errors
Malformed requests
Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request
If you use this project in your research or publications, please cite it as follows:
author = {Moudather Chelbi},
title = {GeneOntology MCP Server},
year = {2025},
howpublished = {https://github.com/Augmented-Nature/GeneOntology-MCP-Server},
note = {Accessed: 2025-06-29}Get detailed information for a specific GO term
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | GO term identifier (e.g., GO:0008150) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information but doesn't describe traits like whether it's read-only (implied by 'Get'), error handling for invalid IDs, rate limits, authentication needs, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Get detailed information'), making it easy to parse. Every part of the sentence earns its place by specifying the resource and scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (retrieving detailed information), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'detailed information' includes (e.g., term name, definition, relationships), potential errors, or response structure. For a tool with these gaps in structured data, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'id' parameter clearly documented as a GO term identifier with an example. The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or validation rules. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information for a specific GO term'). It distinguishes from siblings like 'search_go_terms' (which likely searches multiple terms) and 'validate_go_id' (which validates IDs rather than retrieving information). However, it doesn't explicitly mention what constitutes 'detailed information' or differentiate from 'get_ontology_stats' which might provide broader statistics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid GO term ID), exclusions, or comparisons to sibling tools like 'search_go_terms' for broader queries or 'validate_go_id' for ID validation. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get statistics about GO ontologies (term counts, recent updates)
| Name | Required | Description | Default |
|---|---|---|---|
| ontology | No | Specific ontology or "all" for overall stats |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but lacks critical behavioral details: it doesn't specify if this is a read-only operation (implied but not stated), what format the statistics are returned in, whether there are rate limits, authentication requirements, or how 'recent updates' are defined. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get statistics about GO ontologies') and provides specific examples in parentheses. There's zero wasted text, and it's appropriately sized for a simple tool with one parameter. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional parameter, no output schema, no annotations), the description is incomplete. It lacks information on return format, error handling, or behavioral constraints. While the purpose is clear, the absence of annotations and output schema means the description should provide more context about what the tool returns and how it behaves, which it doesn't do adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'ontology' fully documented in the schema. The description adds no additional parameter semantics beyond what's in the schema—it doesn't elaborate on ontology options, default behavior when parameter is omitted, or examples. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get statistics') and resource ('GO ontologies'), with specific examples of what statistics are retrieved ('term counts, recent updates'). It distinguishes from sibling tools like get_go_term (which retrieves a single term) and search_go_terms (which searches terms), but doesn't explicitly differentiate from validate_go_id. The purpose is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over get_go_term for statistical overviews versus detailed term data, or how it relates to search_go_terms for broader queries. There's no context about prerequisites, timing, or exclusions, leaving usage entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search across Gene Ontology terms by keyword, name, or definition
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (term name, keyword, or definition) | |
| ontology | No | GO ontology to search (default: all) | |
| size | No | Number of results to return (1-500, default: 25) | |
| exact | No | Exact match only (default: false) | |
| include_obsolete | No | Include obsolete terms (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose whether this is a read-only operation, potential rate limits, authentication needs, or what the output format looks like (e.g., list of terms with IDs). The description only states the search action without behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core purpose ('Search across Gene Ontology terms') and adds necessary scope details ('by keyword, name, or definition') without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a search tool with 5 parameters. It doesn't explain what results look like (e.g., structured data with IDs and names), how pagination works (implied by 'size' but not described), or behavioral traits like rate limits. This leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no parameter-specific information beyond implying search scope ('keyword, name, or definition'), which aligns with the schema's 'query' description. Baseline 3 is appropriate as the schema handles parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search across') and resource ('Gene Ontology terms'), specifying the search scope ('by keyword, name, or definition'). It distinguishes from sibling 'get_go_term' (which retrieves specific terms) by emphasizing search functionality, though it doesn't explicitly contrast with 'get_ontology_stats' or 'validate_go_id'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. The description implies usage for searching GO terms, but it doesn't mention when to choose this over 'get_go_term' (e.g., for known IDs vs. keyword searches) or other siblings, nor does it specify prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate GO identifier format and check if term exists
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | GO identifier to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions two behaviors (format validation and existence checking) but doesn't disclose important traits like whether this is a read-only operation (likely, but not stated), what happens with invalid formats (e.g., returns error vs. false), response format, or any rate limits. The description is minimal and leaves behavioral aspects ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (one brief sentence) and front-loaded with all necessary information. Every word earns its place by specifying both validation actions. There's no wasted text or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (validation and existence checking), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., boolean, validation details, error messages) or behavioral nuances. For a tool with no structured output documentation, the description should provide more context about results and usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the 'id' parameter is fully described in the schema as 'GO identifier to validate'), so the baseline is 3. The description adds no additional parameter information beyond what the schema provides (no format examples, no constraints like pattern requirements). It simply restates the parameter's purpose without enhancing understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('validate format' and 'check if term exists') and identifies the resource ('GO identifier'). It distinguishes from sibling tools like 'get_go_term' (which retrieves term details) and 'search_go_terms' (which searches for terms) by focusing on validation and existence checking. However, it doesn't explicitly differentiate from 'get_ontology_stats' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (when you need to validate a GO identifier format and verify term existence), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_go_term' (which might also validate as part of retrieval) or 'search_go_terms' (which might handle partial identifiers). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_go_termget_ontology_statssearch_go_termsvalidate_go_idEach tool has a clearly distinct purpose with no overlap: get_go_term retrieves specific term details, get_ontology_stats provides statistical summaries, search_go_terms enables keyword-based discovery, and validate_go_id handles identifier validation. The descriptions clearly differentiate between retrieving, searching, validating, and statistical analysis functions.
All tools follow a consistent verb_noun pattern with snake_case formatting: get_go_term, get_ontology_stats, search_go_terms, validate_go_id. The naming convention is perfectly uniform throughout the set, making the tools predictable and easy to understand.
Four tools is reasonable for a GeneOntology server, covering core operations like retrieval, search, validation, and statistics. While slightly minimal, each tool serves a distinct purpose and the count aligns well with the domain's typical query patterns. A few additional tools for relationships or annotations could enhance completeness but aren't essential.
The tools cover basic query operations well but have notable gaps for a GeneOntology domain. Missing are tools for term relationships (e.g., get_parents, get_children), annotations (e.g., get_annotations_for_term), or ontology traversal. While agents can work with the provided tools, they cannot perform common GO analysis tasks like exploring term hierarchies or retrieving associated genes.
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