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google-meridian-mcp

google-meridian-mcp

by muhammad1azmi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

audit_model_first_principles

Audit model specification by checking identifiability, knot density, prior variance, and Hill parameter bounds for marketing mix models.

Instructions

Audits model spec for identifiability, knot density, prior variance, and Hill parameter bounds.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "audit_model_first_principles"
  }
}

Input Schema

NameRequiredDescriptionDefault
config_jsonNo{}

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.4/5.0
Behavior2/5

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

No annotations are present, so the description must fully disclose behavior. It only states what is audited but omits critical details: whether the tool returns warnings, errors, or reports, whether it is read-only, and what happens on failure.

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 a single sentence, which is concise, but it sacrifices completeness. Every word is relevant but insufficient to convey necessary details.

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

Completeness2/5

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

Given no output schema, no annotations, and a single optional parameter, the description lacks essential context. The agent cannot infer the tool's place among siblings (e.g., should it be run before or after calculate_bayesian_prior?) or what constitutes a valid input.

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

Parameters1/5

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

The only parameter, config_json, has no description in the schema (0% coverage) and is not explained in the tool description. The agent receives no help understanding how to set this parameter or what values are expected beyond the default empty object.

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 specifies that the tool audits a model spec for identifiability, knot density, prior variance, and Hill parameter bounds. The verb 'audits' is somewhat vague, but listing the audited attributes clarifies the tool's scope and distinguishes it from siblings like calculate_bayesian_prior.

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

Usage Guidelines2/5

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, prerequisites, or alternatives. The description does not explain how this tool fits into a workflow or when it should be preferred over siblings.

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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