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google-meridian-mcp

google-meridian-mcp

by muhammad1azmi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_control_point_guide

Retrieve operational parameters, math formulas, and bounds for data science control points such as adstock decay and hill saturation.

Instructions

Provides operational parameters, math formulas, and bounds for data science control points.

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": "get_control_point_guide"
  }
}

Input Schema

NameRequiredDescriptionDefault
control_pointNodata_inputs, confounders_and_dag, adstock_decay, hill_saturation, bayesian_priors, mcmc_diagnostics, optimization_bounds, or ALLALL

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It states what the tool provides but omits critical details such as return format (e.g., JSON, plain text), error handling, or any side effects (though likely read-only). The user is left unsure what to expect from the output.

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?

The description is a single, efficiently worded sentence that immediately communicates the tool's purpose. No extraneous information is included, and all phrases ('operational parameters', 'math formulas', 'bounds') add value.

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 the tool returns detailed information (formulas, bounds), the description fails to specify the output structure or format. With no output schema and no annotations, the user lacks crucial context about how to interpret the result, making the description insufficient for a complex guide tool.

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% for the single parameter, with clear allowed values already enumerated in the input schema. The description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

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 clearly states the tool provides 'operational parameters, math formulas, and bounds for data science control points', specifying a distinct resource (control points) and action (providing details). This distinguishes it from sibling tools like 'get_mmm_workflow_guide' which focus on workflows, and 'calculate_bayesian_prior' which performs calculations.

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?

The description offers no explicit guidance on when to use this tool versus alternatives. While the purpose implies usage for retrieving control point details, it does not mention when not to use it (e.g., for general documentation or calculations) or highlight sister tools like 'fetch_docs' or 'get_mmm_workflow_guide' as alternatives for different needs.

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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