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GrowthMCP

GrowthMCP

by Lokeshwar2005
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

normalize_growth_records

Transform heterogeneous marketing records into the canonical GrowthMCP schema to enable consistent analytics and reporting.

Instructions

Normalize heterogeneous marketing records into the canonical GrowthMCP schema.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "records": "<records>"
    },
    "name": "normalize_growth_records"
  }
}

Input Schema

NameRequiredDescriptionDefault
sourceNounknown
recordsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations, the description carries the entire behavioral burden, and it discloses nothing about whether the operation is read-only or mutating, whether original records are modified, how unknown source types are handled, or whether unrecognized fields are dropped or preserved.

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

Conciseness4/5

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

It is a single front-loaded sentence with zero filler, so it reads cleanly; the only reservation is that such brevity leaves no room for the behavioral detail an unannotated tool needs.

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?

An output schema exists, so return values need not be explained, but for a tool with two fully undocumented parameters and no annotations the description omits the input-format expectations and normalization-selection semantics that an agent needs to invoke it correctly.

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

Parameters2/5

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

Schema description coverage is 0% for both parameters. The description implies what "records" holds but never states the accepted input shapes, required keys, or what "source" (defaulting to "unknown") changes in the normalization behavior.

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 gives a specific verb ("Normalize"), a clear input class ("heterogeneous marketing records"), and a named target ("the canonical GrowthMCP schema"), which is enough for an agent to distinguish it from the CRUD/analytics siblings in the toolset.

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?

There is no statement of when to reach for this tool versus alternatives, no preconditions, and no mention of what upstream step should produce the records being normalized. Usage is only faintly implied by the word "heterogeneous".

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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