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Noctua MCP Server

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by geneontology

model_summary

Get a summary of a GO-CAM model, including individual count, fact count, and predicate distribution.

Instructions

Get a summary of a GO-CAM model including counts and key information.

Args: model_id: The GO-CAM model identifier

Returns: Summary with individual count, fact count, and predicate distribution

Examples: # Get summary of a model result = model_summary("gomodel:5fce9b7300001215") # Returns: # { # "model_id": "gomodel:5fce9b7300001215", # "state": "production", # "individual_count": 42, # "fact_count": 67, # "predicate_distribution": { # "RO:0002333": 15, # enabled_by (note: not in vetted list) # "RO:0002411": 8, # causally upstream of # "BFO:0000066": 12, # occurs_in # "BFO:0000050": 5 # part_of # } # }

# Check if a model is empty
result = model_summary("gomodel:new_empty_model")
if result["individual_count"] == 0:
    print("Model is empty")

# Analyze model complexity
result = model_summary("gomodel:12345")
causal_edges = result["predicate_distribution"].get("RO:0002411", 0)
causal_edges += result["predicate_distribution"].get("RO:0002413", 0)  # provides input for
causal_edges += result["predicate_distribution"].get("RO:0002629", 0)  # directly positively regulates
causal_edges += result["predicate_distribution"].get("RO:0002630", 0)  # directly negatively regulates
print(f"Model has {causal_edges} causal relationships")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description must disclose behavior. It indicates a read operation and shows the return structure via examples. However, it does not mention authentication requirements, rate limits, or any side effects. The transparency is adequate but incomplete.

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

Conciseness3/5

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

The description is relatively long due to extensive examples and a docstring format. While structured with Args, Returns, and Examples sections, it could be more concise by reducing example redundancy. The essential information is present but at the cost of brevity.

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

Completeness4/5

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

Given the tool has an output schema (indicated by context signals), the description's return value explanation via examples is sufficient. For a simple retrieval tool with one parameter, the description covers the model ID input and key output fields. No critical gaps are apparent.

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

Parameters4/5

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

The input schema has zero description coverage (no property descriptions), but the tool's description includes an Args section that defines the parameter `model_id` as 'The GO-CAM model identifier'. This adds meaningful context beyond the schema's type-only definition. Examples further illustrate usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a summary of a GO-CAM model with counts and key information. It specifies the resource (GO-CAM model) and action (get summary), making the purpose clear. However, it does not explicitly differentiate from the sibling 'get_model' tool, leaving potential ambiguity about when to use this vs. the full model retrieval.

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

Usage Guidelines3/5

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

The description provides multiple examples that implicitly guide usage, such as checking if a model is empty or analyzing complexity. However, it lacks explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. The usage context is implied but not formally stated.

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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