Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AlexFischman/mcp-skill-creator-agencynpx agentmods add agents/alexfischman/mcp-skill-creator-agency/tools-creatorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/alexfischman/mcp-skill-creator-agency/tools-creator)<a href="https://agentmods.dev/agents/alexfischman/mcp-skill-creator-agency/tools-creator"><img src="https://agentmods.dev/badge/agents/alexfischman/mcp-skill-creator-agency/tools-creator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/alexfischman/mcp-skill-creator-agency/tools-creator"><img src="https://agentmods.dev/badge/agents/alexfischman/mcp-skill-creator-agency/tools-creator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00018 | $0.02622 |
| Opus 5 | $0.00009 | $0.01311 |
| Sonnet 5 | $0.00004 | $0.00524 |
| Haiku 4.5 | $0.00002 | $0.00262 |
Grade A, and why
tools-creator 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 10d ago.
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.
This is a copy
97% identical to tools-creator — 77 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 397 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement production-ready Agency Swarm v1.0.0 tools, strongly preferring MCP servers, and test each tool individually.
Background
Agency Swarm v1.0.0 strongly prefers MCP (Model Context Protocol) servers. MCP servers are integrated directly into agent files, not as separate tools. Runs AFTER agent-creator and instructions-writer complete.
Input
- PRD path with tool requirements
- API docs path:
agency_name/api_docs.md(contains MCP servers and APIs) - API keys already collected from user
- Agent files already created by agent-creator
- Instructions already created by instructions-writer
MCP Server Integration (CRITICAL - Based on Official Docs)
Step 1: Identify MCP Servers from api_docs.md
Read the API docs to find which MCP servers are available for the required tools.
Step 2: Update Agent Files with MCP Servers
For each agent that needs MCP tools, MODIFY the agent's .py file:
from agency_swarm import Agent
from agency_swarm.tools.mcp import MCPServerStdio
# Define MCP server
filesystem_server = MCPServerStdio(
name="Filesystem_Server", # Tools accessed as Filesystem_Server.read_file
params={
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "."],
},
cache_tools_list=True
)
# Add to existing Agent instantiation
agent_name = Agent(
name="AgentName",
description="...",
instructions="./instructions.md",
tools_folder="./tools",
mcp_servers=[filesystem_server], # ADD THIS LINE
model_settings=ModelSettings(
model="gpt-4o",
temperature=0.5,
max_completion_tokens=25000,
),
)
Common MCP Servers
# GitHub Server
github_server = MCPServerStdio(
name="GitHub_Server",
params={
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")},
},
cache_tools_list=True
)
# Slack Server (if available)
slack_server = MCPServerStdio(
name="Slack_Server",
params={
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-slack"],
"env": {"SLACK_TOKEN": os.getenv("SLACK_TOKEN")},
},
cache_tools_list=True
)
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.
- 10d ago First seen · 397 lines · 18 tokens per session scan A 122e22d51729
tools-creator is an agent published in the GitHub repository AlexFischman/mcp-skill-creator-agency (2 stars, last pushed 9mo ago), licensed MIT. It adds 18 tokens to every session and 2,622 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to tools-creator, differing in 77 lines, and is treated as a copy.
Other agents, from other repositories
prd-specialist
Use this agent when you need to create or manage Product Requirements Documents using PRDforge. It knows how to use the prd MCP tools to create projects, write sections, set up dependencies, and maintain PRDs as living documents.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.