Skip to main content
Glama

compare_models

Compare multiple NONMEM pharmacometric models using objective function value (OFV), delta-OFV, parameter counts, and AIC criteria to identify optimal model performance.

Instructions

Compare multiple NONMEM runs by OFV, delta-OFV, parameter counts, and AIC. Accepts a list of run directories or .ext file paths.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsYesList of models to compare
Behavior3/5

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

With no annotations provided, the description carries the full burden. It successfully discloses what calculations are performed (delta-OFV, AIC), but fails to state whether the operation is read-only, requires completed runs, or describe the return format.

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

Conciseness5/5

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

Two well-structured sentences with zero waste: the first states purpose and metrics, the second specifies input requirements. Information is front-loaded with the action verb.

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?

For a single-parameter comparison tool with full schema coverage, the description is nearly complete. It could be improved by noting the read-only nature or output structure, but adequately describes the core functionality without requiring verbosity.

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

Parameters3/5

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

Schema description coverage is 100%, establishing a baseline of 3. The description adds semantic context that the inputs represent model runs and file paths, though it loosely mentions 'run directories' while the schema strictly specifies '.ext' file paths.

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?

The description uses specific verbs ('Compare') and resources ('NONMEM runs'), and explicitly lists comparison metrics (OFV, delta-OFV, parameter counts, AIC). This clearly distinguishes it from single-run siblings like 'summarize_run' or 'get_run_results'.

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 implies usage context through the specific statistical metrics mentioned (suggesting use for model selection/comparison), but provides no explicit 'when to use' guidance or contrasts with alternatives like 'read_ext_file' for individual file inspection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sueinchoi/nonmem-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server