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AIMFP

AIMFP

by aryanduntley
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Python
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get_user_custom_json_parameters

Read-onlyIdempotent

Fetch available fields for a specified table, returning field names, data types, and required flags for use in generic add or update operations.

Instructions

Get available fields for table to use with generic add/update operations. Returns field names, types, and whether required.

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": {
      "table": "<table>"
    },
    "name": "get_user_custom_json_parameters"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYesTable name to get available fields for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.37.0

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description does not need to reiterate safety. It adds value by specifying the return content (field names, types, required), but no additional behavioral traits are disclosed.

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 two sentences, front-loading the purpose and immediately listing return information. Every sentence is necessary and efficiently conveys the tool's function.

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 simple read-only tool with one parameter and no output schema, the description provides sufficient context: what it does, what it returns, and and when to use it. It could mention that it applies to user_custom tables, but the name already implies that.

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?

The single parameter 'table' is fully described in the input schema (100% coverage). The description does not add new semantic details beyond the schema, meeting the baseline expectation.

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 gets available fields for a table, specifying the purpose is to use with generic add/update operations. It distinguishes from sibling tools like add/update operations by clarifying the pre-usage context.

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?

The description indicates when to use the tool: before generic add/update operations. However, it does not explicitly exclude alternatives or mention when not to use it, but the context is clear.

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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