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AIMFP

by aryanduntley
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Python
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reserve_functions

Batch-reserve unique function IDs before writing multiple functions to avoid conflicts. Specify each function's name, file, purpose, parameters, and returns.

Instructions

Batch-reserve function IDs before writing multiple functions. Include purpose/parameters/returns for every function.

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": {
      "functions": "<functions>"
    },
    "name": "reserve_functions"
  }
}

Input Schema

NameRequiredDescriptionDefault
functionsYesArray of function objects [{name, file_id, purpose, parameters, returns, skip_id_naming?}, ...]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.37.0

TDQS

A4.2/5.0
Behavior3/5

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

Annotations are minimal (no readOnly, not destructive), so the description carries the burden. It adds that each function should include purpose/parameters/returns, which is helpful. However, it does not explain what 'reserve' entails (e.g., locking, allocation) or side effects beyond reservation.

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 18-word sentence, efficient and front-loaded. It wastes no words and clearly conveys the action and data requirements.

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 the tool's simplicity (one parameter, no output schema), the description is largely sufficient. It could briefly mention the return value (e.g., 'returns reserved IDs'), but the absence is not critical for a straightforward batch operation.

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% with one parameter. The description adds value by specifying that each function object must include 'purpose/parameters/returns', which goes beyond the schema's field names. This guides the agent on required content.

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 action ('Batch-reserve function IDs'), the resource ('function IDs'), and the context ('before writing multiple functions'). It distinguishes this tool from siblings like 'reserve_function' (singular) and other reserve tools.

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 explicitly says 'before writing multiple functions', indicating when to use. Although it doesn't mention when not to use or name alternatives explicitly, the batch vs singular distinction is clear from sibling tool names.

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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