Borrowing it
Nothing to install: this file belongs to wheattoast11/openrouter-deep-research-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/wheattoast11/openrouter-deep-research-mcp/main/.claude/commands/mcp-search.mdgit clone --depth 1 https://github.com/wheattoast11/openrouter-deep-research-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/wheattoast11/openrouter-deep-research-mcp/mcp-search)<a href="https://agentmods.dev/commands/wheattoast11/openrouter-deep-research-mcp/mcp-search"><img src="https://agentmods.dev/badge/commands/wheattoast11/openrouter-deep-research-mcp/mcp-search/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/wheattoast11/openrouter-deep-research-mcp/mcp-search"><img src="https://agentmods.dev/badge/commands/wheattoast11/openrouter-deep-research-mcp/mcp-search.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 | $0.00128 |
| Opus 5 | $0.00000 | $0.00064 |
| Sonnet 5 | $0.00000 | $0.00026 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
mcp-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
MCP Search
Search existing knowledge base (reports + docs).
Steps
search({ q: "$ARGUMENTS", k: 10, scope: "both" })- Summarize matches with relevance scores
- For details:
get_report({ reportId: "<id>" })
Options
| Param | Values | Default |
|---|---|---|
| k | 1-100 | 10 |
| scope | "both", "reports", "docs" | both |
| rerank | true/false | false |
Query
$ARGUMENTS
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.
- 11d ago First seen · 21 lines · 0 tokens per session scan A 92d4e5a9d2e2
mcp-search is a command published in the GitHub repository wheattoast11/openrouter-deep-research-mcp (55 stars, last pushed 10d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 128 tokens. 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 2026-08-30.
Other commands, from other repositories
build-corpus
Use when the user invokes /goal-flight build-corpus to build or refresh the private Goal Flight dispatch-context corpus.
ingest
Manually add knowledge to the Weaviate store.
new-rag
Scaffold a new RAG (Retrieval-Augmented Generation) pipeline with best practices.
index
Index a folder of documents for semantic search.
ingest
Ingest a URL, directory, or file into your knowledge base. remember = a specific durable fact, ingest = a URL, learn = a distilled lesson that gets retrieval preference.
checklist
Generate a custom checklist for the current feature based on user requirements.