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 SECRET4422/mcp-deep-research-server --skill deep-researchgit clone --depth 1 https://github.com/SECRET4422/mcp-deep-research-serverWrote 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/secret4422/mcp-deep-research-server/deep-research)<a href="https://agentmods.dev/skills/secret4422/mcp-deep-research-server/deep-research"><img src="https://agentmods.dev/badge/skills/secret4422/mcp-deep-research-server/deep-research/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/secret4422/mcp-deep-research-server/deep-research"><img src="https://agentmods.dev/badge/skills/secret4422/mcp-deep-research-server/deep-research.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 | $0.00064 | $0.01280 |
| Opus 5 | $0.00032 | $0.00640 |
| Sonnet 5 | $0.00013 | $0.00256 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
deep-research 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 6d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Agent Skill
This skill guides AI agents on orchestrating multi-step, evidence-backed research using the tools exposed by mcp-deep-research-server.
When to Use
- When the user asks for in-depth, cited research on a technology, company, scientific topic, or market trend.
- When an objective fact-check is requested to verify controversial claims or rumors.
- When comparing 2–5 distinct URLs or source narratives to identify consensus vs contradictions.
- When saving or retrieving structured research memory across long-running tasks.
Tool Reference & Routing
| Operation | Primary Tool | Description |
|---|---|---|
| Full Investigation | deep_research |
Automated pipeline: searches DuckDuckGo, parallel-scrapes 3–8 pages, extracts stats/entities, synthesizes cited report. |
| Targeted Query | search_web |
Clean search (1–10 results) with time filtering (day, week, month, year). |
| Page Deep-Dive | scrape_page |
Cleans HTML into Turndown markdown, extracts main content, headings, and links. SSRF-safe. |
| Cross-Source Analysis | compare_sources |
Scrapes 2–5 URLs in parallel, surfaces common entities, shared consensus, and contradictory claims. |
| Verification & Audit | fact_check_claim |
Searches supporting and debunking sources, returning an evidence-backed heuristic verdict. |
| Extract Key Points | extract_insights |
Heuristic scoring of statistics, named entities, key sentences, and answered questions. |
| Long-Term Memory | memory_save / memory_search |
Persistent storage in ~/.mcp-deep-research/memory.json for cross-session recall. |
Autonomous Research Workflow
Follow this 5-stage discipline for every deep investigation:
graph TD
A[User Request] --> B[1. Check Memory: memory_search]
B --> C[2. Formulate 2-3 Multi-Angle Queries]
C --> D[3. Run deep_research or search_web + parallel scrape]
D --> E[4. Cross-Examine: compare_sources / fact_check]
E --> F[5. Synthesize & Cite with URL Provenance]
F --> G[6. Persist Key Findings: memory_save]
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.
- 6d ago First seen · 119 lines · 64 tokens per session scan A 0eaf41feeb7f
deep-research is a skill published in the GitHub repository SECRET4422/mcp-deep-research-server (1 stars, last pushed 9d ago), licensed MIT. It adds 64 tokens to every session and 1,280 once invoked, about $0.0003 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 2026-09-04.
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