A command that creates an implementation plan for 3Commas API integrations. An API is a defined way for software to request data or actions from another service.

Use it to plan new 3Commas API support, validate endpoint parameters and error responses, identify existing implementation patterns, and outline models and documentation work.

It reduces uncertainty about which API endpoints, parameters, request formats, and responses the implementation should use. It also checks the project documentation and test scripts before coding.

How severity1/3commas-mcp configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to severity1/3commas-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/severity1/3commas-mcp/main/.claude/commands/plan.md

Clone the repo

git clone --depth 1 https://github.com/severity1/3commas-mcp

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/severity1/3commas-mcp/plan/github.svg)](https://agentmods.dev/commands/severity1/3commas-mcp/plan)

Your own site

<a href="https://agentmods.dev/commands/severity1/3commas-mcp/plan"><img src="https://agentmods.dev/badge/commands/severity1/3commas-mcp/plan/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/severity1/3commas-mcp/plan"><img src="https://agentmods.dev/badge/commands/severity1/3commas-mcp/plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 705 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00012	$0.00705
Opus 5.5	$0.00005	$0.00282
Sonnet 5.5	$0.00002	$0.00141
Haiku 4.5	$0.00001	$0.00071

Measured 2mo ago against content hash 7d342f12af11, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

plan 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 2mo 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.

.claude/commands/plan.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

API Implementation Plan: $ARGUMENTS

Phase 1: Analysis & Validation

API Discovery

Examine project context and identify API details:

Check docs/MVP_GET_APIS.md for priority, phase, and endpoint details
Review docs/API_REFERENCES.md for current implementation status
Analyze TASKS.md for progress context and next logical steps
Identify best reference implementation to copy patterns from

Parameter Validation

Validate API using testing scripts:

Test endpoint: python scripts/test_api.py <endpoint> with realistic parameters
Parameter Testing: Identify required vs optional parameters, valid parameter names, correct value formats and types, parameter constraints
Request/Response Validation: Confirm request method and parameter location, test parameter variations, verify actual vs documented response structure
Error Testing: Test invalid parameters to understand error responses
Document actual response structure and exact parameter names
Verify token count < 25,000 for MCP efficiency

Phase 2: Implementation

Setup & Model Creation

TodoWrite: Create todos for implementation and documentation phases
Pydantic Model (models/{domain}.py):
- Use ONLY script-validated parameter names, types, and constraints
- Follow @docs/PATTERNS.md model patterns exactly
- Inherit from APIRequest with proper field validation

Tool Function & Registration

Tool Function (tools/{domain}.py):
- Follow @docs/PATTERNS.md tool patterns exactly
- Use @handle_api_errors decorator
- Include response_filter parameter with "display" default
- Use validated endpoint path and parameters from testing
Registration: Add mcp.tool()(domain.function_name) to server.py

Quality Assurance

Validation: Run all quality checks: uv run -m black . && uv run -m ruff format . && uv run -m ruff check . && uv run -m mypy .
Final Test: Verify implemented function matches script validation results

Read the full file on GitHub · 68 lines

Changes

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.

2mo ago First seen · 68 lines · 12 tokens per session scan A 7d342f12af11

Subscribe to this mod's changes

plan is a command published in the GitHub repository severity1/3commas-mcp (3 stars, last pushed 1y ago), licensed MIT. It adds 12 tokens to every session and 705 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-08-31.

Backend and APIs Planning

plan next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
planseverity1/3commas-mcp	Command	3	1y ago	MIT	A	12	Claude Code
initfcakyon/claude-codex-settings	Command	1.2k	yesterday	Apache-2.0	A	0	Claude Code
http-serviceMadAppGang/magus	Command	14	yesterday	MIT	A	27	Claude Code
buildAzure/ai-gateway	Command	5	10d ago	MIT	A	0	Claude Code
api-contract-reviewMozurok/fhorja.dev	Command	10	28d ago	MIT	A	64	Claude Code
start-10-1minicoohei/ai-agent-camp	Command	347	1mo ago	none	A	2	Claude Code
fastapimfmezger/ai_agent_dotfiles	Command	7	3mo ago	MIT	A	84	Claude Code, Gemini CLI

3commas-mcp: Command for Claude Code