Wonderwhy-er/DesktopCommanderMCP is an MCP server that lets AI clients search and edit files, run terminal commands, and manage computer processes. It is used to give coding agents practical control over a local development environment through chat. The catalogue add-ons extend or configure this server and its agent workflows.
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 skills add wonderwhy-er/DesktopCommanderMCP --skill ai-tools-setupgit clone --depth 1 https://github.com/wonderwhy-er/DesktopCommanderMCPWrote 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/skills/wonderwhy-er/desktopcommandermcp/ai-tools-setup)<a href="https://agentmods.dev/skills/wonderwhy-er/desktopcommandermcp/ai-tools-setup"><img src="https://agentmods.dev/badge/skills/wonderwhy-er/desktopcommandermcp/ai-tools-setup/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/skills/wonderwhy-er/desktopcommandermcp/ai-tools-setup"><img src="https://agentmods.dev/badge/skills/wonderwhy-er/desktopcommandermcp/ai-tools-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 64 Skill accesses MCP server configuration files (mcp.json). MCP configs contain server URLs, authentication tokens, and tool definitions — reading them allows the skill to discover and potentially abuse other tool integrations.Fix: Remove all code or instructions that read MCP configuration files (mcp.json). MCP server details should be managed by the agent runtime, not read by individual skills.
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 | $0.00168 | $0.01413 |
| Opus 5 | $0.00084 | $0.00707 |
| Sonnet 5 | $0.00034 | $0.00283 |
| Haiku 4.5 | $0.00017 | $0.00141 |
Grade A, and why
ai-tools-setup scanned grade A 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl to the provider) early, and tell the user plainly if the key is invalid Copies of this mod
2 near-identical copies found in the catalogue:
- ai-tools-setup — 100% identical, 0 lines differ
- ai-tools-setup — 98% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Tools Setup Assistant
Help users install, configure, and repair AI tooling on their own machine using Desktop Commander's file and terminal tools. The work is usually configure-and-troubleshoot, not clean installs — assume something already exists and may be half-broken.
Golden rules (read before doing anything)
- Detect the OS and shell first. Read
get_configand use itssystemInfoandallowedDirectories— file paths, commands, and config locations differ across macOS, Windows, and Linux, so confirm the platform before assuming anything. - Verify the model/API key works before you build. The most common reason
these sessions dead-end is a provider auth or billing error discovered
after a lot of setup work. Make one tiny test call with
start_process(a quick curl to the provider) early, and tell the user plainly if the key is invalid or out of credit. - Translate errors, don't dump them. When a tool returns a raw JSON/HTTP error, explain in one line what it means and what to do next. If a model is missing, deprecated, rate-limited, or doesn't support tool-calling, suggest a concrete working alternative instead of retrying the same thing.
- Never echo secrets. API keys, tokens, and passwords go into the right
config or
.envfile — never repeated back in chat. If a user pastes a live secret, warn them, use it, and suggest they rotate it if it was shared insecurely. Redact secrets in any output, logs, or summaries you produce. - Back up before you edit. Copy the config to a
.bakfirst withstart_process(cp file.json file.json.bak, orcopyon Windows), then make the change withedit_blockso existing entries survive — reach forwrite_fileonly on a from-scratch config. Validate the JSON after every edit by parsing it withstart_process. - Verify, then stop. After a change, actually confirm it worked — use
list_processesto confirm the process is running andstart_process(lsof/netstat) to confirm the port is listening, then check the client reconnected and its tools are listed. Don't declare success on a guess. - Make it repeatable. Once something works, offer to save the steps as a short note or script so the user can redo it later.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 108 lines · 168 tokens per session scan A 77895640c33b
ai-tools-setup is a skill published in the GitHub repository wonderwhy-er/DesktopCommanderMCP (9,513 stars, last pushed yesterday), licensed MIT. It adds 168 tokens to every session and 1,413 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
peer-review
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
scientific-schematics
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways…
hypothesis-generation
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for…
paper-lookup
Search 10 academic literature APIs for papers, preprints, citations, and open-access full text, and return results with reproducible provenance. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID/arXiv…
scholar-evaluation
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.