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 commands/severity1/3commas-mcp/plangit clone --depth 1 https://github.com/severity1/3commas-mcpWhat 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 | $0.00705 |
| Opus 5 | $0.00006 | $0.00352 |
| Sonnet 5 | $0.00002 | $0.00141 |
| Haiku 4.5 | $0.00001 | $0.00071 |
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 2d 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.
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.mdfor priority, phase, and endpoint details - Review
docs/API_REFERENCES.mdfor current implementation status - Analyze
TASKS.mdfor 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)toserver.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
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.
- 2d ago First seen · 68 lines · 12 tokens per session scan A 7d342f12af11
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 $0.0001 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.