Skip to main content
Glama
Axiomatic-AI

axiomatic-mcp

Official
by Axiomatic-AI

AxModelFitter_compute_parameter_covariance

Compute parameter covariance matrices and standard errors for fitted models using robust Huber-White and inverse Hessian estimators, providing uncertainty estimates and correlation analysis.

Instructions

Compute parameter covariance matrices for fitted model parameters.

Provides uncertainty estimates using robust Huber-White sandwich estimator and
classical inverse Hessian approach. Use after fit_model to quantify parameter
uncertainty and correlations.

REQUIRED: Fitted parameters, model definition, same data used in fitting, variance estimate.
RETURNS: Covariance matrices, standard errors, correlation matrix.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
boundsYesALL parameter/input/output bounds: [{'name': 'a', 'lower': {'magnitude': 0, 'unit': 'dimensionless'}, 'upper': {'magnitude': 10, 'unit': 'dimensionless'}}]
varianceNoNoise variance (σ²) for uncertainty quantification. Estimate from residuals or domain knowledge. (estimated from loss if None)
constantsNoFixed constants: [{'name': 'c', 'value': {'magnitude': 3.0, 'unit': 'meter'}}]
data_fileYesPath to data file (CSV, Excel, JSON, Parquet). All data must be provided via file.
docstringNoBrief description of the model
input_dataYesInput column mappings: [{'column': 'time', 'name': 't', 'unit': 'second'}, {'column': 'x_col', 'name': 'x', 'unit': 'meter'}]
model_nameYesModel name (e.g., 'ExponentialDecay', 'RingResonator')
parametersYesFitted parameter values: [{'name': 'a', 'value': {'magnitude': 2.0, 'unit': 'dimensionless'}}]
file_formatNoFile format: 'csv', 'excel', 'json', 'parquet' (auto-detect if None)
jit_compileNoEnable JIT compilation for performance
output_dataYesOutput column mapping: {'columns': ['signal'], 'name': 'y', 'unit': 'volt'} OR {'columns': ['y1', 'y2'], 'name': 'y', 'unit': 'volt'}
scale_paramsNoEnable parameter scaling for numerical stability
function_nameYesFunction name that computes the model output
function_sourceYesJAX function source code. MUST use jnp operations: jnp.exp, jnp.sin, etc.
cost_function_typeNoCost function: 'mse' (default), 'mae'mse
Behavior3/5

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

No annotations provided, so description carries full burden. Discloses use of two estimators and return values, but does not mention side effects, computational cost, or assumptions like numerical stability. Adequate but not comprehensive.

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?

Extremely concise: three brief sections (main verb, method details, required/returns). Each sentence serves a purpose with no redundancy. Information is front-loaded.

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 15 parameters, nested objects, and no output schema, description covers the main purpose, required items, return types, and estimator methods. Could add example usage or explain impact of cost_function_type, but overall adequate.

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?

Schema coverage is 100% (baseline 3). Description adds value by stating default behavior for 'variance' parameter (estimated from loss if None) and summarizing return structure (covariance matrices, standard errors, correlation matrix).

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?

Clearly states the tool computes parameter covariance matrices for fitted model parameters. Specifies two methods (robust Huber-White, classical inverse Hessian) and explicitly says to use after fit_model, distinguishing it from the fitting step.

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?

Explicitly says 'Use after fit_model' and lists required items (fitted parameters, model definition, etc.). Provides clear prerequisites but does not explicitly exclude alternatives or mention when not to use.

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/Axiomatic-AI/ax-mcp'

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