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geneontology

Noctua MCP Server

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by geneontology

add_individual

Add an instance of a class to a GO-CAM model with label validation that prevents incorrect IDs by automatically rolling back mismatches.

Instructions

Add an individual (instance) of a class to a GO-CAM model with label validation.

This tool requires providing the expected label for the class to prevent accidental use of wrong IDs (e.g., GO:0003924 vs GO:0003925). The operation will automatically rollback if the created individual doesn't match the expected label.

Args: model_id: The GO-CAM model identifier (e.g., "gomodel:12345") class_curie: The class to instantiate (e.g., "GO:0003674") class_label: The expected rdfs:label of the class (e.g., "molecular_function") assign_var: Variable name for referencing in the same batch

Returns: Barista API response with message-type and signal fields. If validation fails, includes rolled_back=true and validation error.

Examples: # Add a molecular function activity with validation add_individual("gomodel:12345", "GO:0004672", "protein kinase activity", "mf1")

# Add a protein/gene product with validation
add_individual("gomodel:12345", "UniProtKB:P38398", "BRCA1", "gp1")

# Add a cellular component with validation
add_individual("gomodel:12345", "GO:0005737", "cytoplasm", "cc1")

# Add a biological process with validation
add_individual("gomodel:12345", "GO:0016055", "Wnt signaling pathway", "bp1")

# Add an evidence instance with validation
add_individual("gomodel:12345", "ECO:0000353", "physical interaction evidence", "ev1")

# Variables like "mf1", "gp1" can be referenced in subsequent
# add_fact calls within the same batch operation

Notes: - The label acts as a checksum to prevent ID hallucination - If the label doesn't match, the operation is automatically rolled back - This prevents corrupt models from incorrect IDs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
class_curieYes
class_labelYes
assign_varNox1

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but description fully discloses behavior: automatic rollback on label mismatch, validation logic, return fields including rolled_back. This goes beyond minimal requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is structured with sections (Args, Returns, Examples, Notes) and is front-loaded with purpose. Some redundancy in examples, but overall efficient for a complex tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and 4 parameters, description covers all essential aspects: validation, rollback, variable referencing, response structure. Output schema exists, so return values are supplemented.

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

Parameters5/5

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

Schema coverage is 0%, but description compensates with detailed Args section explaining each parameter, including label as checksum and assign_var for referencing. Examples further clarify usage.

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?

Description clearly states the verb 'add' and resource 'individual (instance) of a class' for GO-CAM models, with label validation. Distinguishes from sibling tools like add_protein_complex and add_entity_set by specifying validation behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains when to use: adding an individual with label validation to prevent wrong IDs. Provides examples for various types. Does not explicitly mention when not to use or compare to alternatives, but context is clear.

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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