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.
git clone --depth 1 https://github.com/aaditya0602/toolplan-mcpWrote 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/commands/aaditya0602/toolplan-mcp/codex-toolplan)<a href="https://agentmods.dev/commands/aaditya0602/toolplan-mcp/codex-toolplan"><img src="https://agentmods.dev/badge/commands/aaditya0602/toolplan-mcp/codex-toolplan/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/commands/aaditya0602/toolplan-mcp/codex-toolplan"><img src="https://agentmods.dev/badge/commands/aaditya0602/toolplan-mcp/codex-toolplan.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 | $0.00000 | $0.00188 |
| Opus 5.5 | $0.00000 | $0.00075 |
| Sonnet 5.5 | $0.00000 | $0.00038 |
| Haiku 4.5 | $0.00000 | $0.00019 |
Grade B, and why
codex-toolplan scanned grade B with 1 finding 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 1mo 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
If the server is missing, tell me to add to ~/.codex/config.toml: What it actually says
/toolplan for OpenAI Codex CLI — copy this file to ~/.codex/prompts/toolplan.md
Call the plan_project tool on the toolplan MCP server with:
idea: the text I passed after the commandgrade: "industry" for production/business apps, otherwise "personal"; ask me if ambiguous.
Run the searches in the result's "Live Crosscheck" section if you have web access; append clearly-better findings under "Fresh findings (unverified)" with source links. Then show the final enriched prompt verbatim in a markdown block. Do not start building until I confirm or hand back an edited version.
If the server is missing, tell me to add to ~/.codex/config.toml:
[mcp_servers.toolplan]
command = "npx"
args = ["-y", "toolplan-mcp"]
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.
- 1mo ago First seen · 21 lines · 0 tokens per session scan B d090fd57a2c1
codex-toolplan is a command published in the GitHub repository aaditya0602/toolplan-mcp (0 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 188 tokens. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.