Borrowing it
Nothing to install: this file belongs to thhart/fd-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.
curl -O https://raw.githubusercontent.com/thhart/fd-mcp/main/.claude/commands/demo-mcp.mdgit clone --depth 1 https://github.com/thhart/fd-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/thhart/fd-mcp/demo-mcp)<a href="https://agentmods.dev/commands/thhart/fd-mcp/demo-mcp"><img src="https://agentmods.dev/badge/commands/thhart/fd-mcp/demo-mcp/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/thhart/fd-mcp/demo-mcp"><img src="https://agentmods.dev/badge/commands/thhart/fd-mcp/demo-mcp.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 | $0.00010 | $0.00181 |
| Opus 5 | $0.00005 | $0.00090 |
| Sonnet 5 | $0.00002 | $0.00036 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
demo-mcp 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 8d 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.
What it actually says
Demonstrate the power of fd-mcp tools by running several quick examples:
- Fast file search: Use mcp__fd__fd_search to find Python files (extension="py")
- Content search: Use mcp__fd__fd_search_content to find "def " in Python files with context
- Recent files: Use mcp__fd__fd_recent_files to show files changed in last 24 hours
- File count: Use mcp__fd__fd_count to count total files
For each operation, explain:
- Which tool you're using
- Why it's better than bash alternatives
- The speed advantage (5-10x faster for search, 10-100x for content search)
Show the user how easy and fast these tools are!
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.
- 8d ago First seen · 18 lines · 10 tokens per session scan A 93e86eba4dd2
demo-mcp is a command published in the GitHub repository thhart/fd-mcp (1 stars, last pushed 9mo ago), licensed MIT. It adds 10 tokens to every session and 181 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
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.