Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/managedcode/prompterone/function-tools-approvalsgit clone --depth 1 https://github.com/managedcode/PrompterOneWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00012 | $0.02245 |
| Opus 5 | $0.00006 | $0.01123 |
| Sonnet 5 | $0.00002 | $0.00449 |
| Haiku 4.5 | $0.00001 | $0.00225 |
Grade A, and why
function-tools-approvals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- function-tools-approvals — 100% identical, 0 lines differ
- function-tools-approvals — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using function tools with human in the loop approvals
::: zone pivot="programming-language-csharp"
This tutorial step shows you how to use function tools that require human approval with an agent, where the agent is built on the Azure OpenAI Chat Completion service.
When agents require any user input, for example to approve a function call, this is referred to as a human-in-the-loop pattern. An agent run that requires user input, will complete with a response that indicates what input is required from the user, instead of completing with a final answer. The caller of the agent is then responsible for getting the required input from the user, and passing it back to the agent as part of a new agent run.
Prerequisites
For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.
Create the agent with function tools
When using functions, it's possible to indicate for each function, whether it requires human approval before being executed.
This is done by wrapping the AIFunction instance in an ApprovalRequiredAIFunction instance.
Here is an example of a simple function tool that fakes getting the weather for a given location.
using System;
using System.ComponentModel;
using System.Linq;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI;
[Description("Get the weather for a given location.")]
static string GetWeather([Description("The location to get the weather for.")] string location)
=> $"The weather in {location} is cloudy with a high of 15°C.";
To create an AIFunction and then wrap it in an ApprovalRequiredAIFunction, you can do the following:
AIFunction weatherFunction = AIFunctionFactory.Create(GetWeather);
AIFunction approvalRequiredWeatherFunction = new ApprovalRequiredAIFunction(weatherFunction);
When creating the agent, you can now provide the approval requiring function tool to the agent, by passing a list of tools to the AsAIAgent method.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 244 lines · 12 tokens per session scan A d789f72836eb
function-tools-approvals is an agent published in the GitHub repository managedcode/PrompterOne (42 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 2,245 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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