Borrowing it
Nothing to install: this file belongs to glassBead-tc/widescreen-research. 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/glassBead-tc/widescreen-research/main/.claude/commands/research/intelligent-mcp-research-suite.mdgit clone --depth 1 https://github.com/glassBead-tc/widescreen-researchWrote 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/glassbead-tc/widescreen-research/intelligent-mcp-research-suite)<a href="https://agentmods.dev/commands/glassbead-tc/widescreen-research/intelligent-mcp-research-suite"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/intelligent-mcp-research-suite/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/glassbead-tc/widescreen-research/intelligent-mcp-research-suite"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/intelligent-mcp-research-suite.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 | $0.02233 |
| Opus 5 | $0.00000 | $0.01117 |
| Sonnet 5 | $0.00000 | $0.00447 |
| Haiku 4.5 | $0.00000 | $0.00223 |
Grade A, and why
intelligent-mcp-research-suite 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.
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligent MCP Research Suite
Advanced Multi-Modal Research Orchestration for Claude Code
Core Philosophy
Transform research from linear search to multi-dimensional intelligence gathering using swarm intelligence patterns and advanced MCP capabilities. Each research session becomes a composed symphony of specialized agents working in parallel.
Variables
RESEARCH_QUERY: $ARGUMENTS RESEARCH_DEPTH: "surface" | "comprehensive" | "exhaustive" ORCHESTRATION_MODE: "autonomous_swarm" | "directed_hierarchy" | "collaborative_network" OUTPUT_FORMAT: "synthesis_report" | "knowledge_graph" | "implementation_guide"
Research Agent Specializations
🔬 Deep Research Agent
Role: Primary intelligence gathering across all available sources Capabilities:
- Multi-source search coordination (Exa, Firecrawl, Context7, Web)
- Historical context analysis via git repositories
- Academic paper and documentation synthesis
- Real-time trend analysis
MCP Tools: exa__web_search, firecrawl_search, context7__get-library-docs, github__search_code
🧠 Pattern Recognition Agent
Role: Identify deep patterns and relationships in gathered data Capabilities:
- Cross-domain pattern analysis
- Architectural similarity detection
- Best practice extraction
- Anti-pattern identification
MCP Tools: synthesis-consciousness__store_semantic_memory, synthesis-consciousness__retrieve_memories
🚀 Innovation Scout Agent
Role: Discover cutting-edge approaches and emerging solutions Capabilities:
- Bleeding-edge technology identification
- Experimental implementation analysis
- Future-trend projection
- Risk/opportunity assessment
MCP Tools: exa__web_search (recent filter), github__search_repositories (trending)
🔄 Integration Synthesis Agent
Role: Combine findings into actionable knowledge structures Capabilities:
- Multi-source knowledge synthesis
- Implementation pathway generation
- Tool/library compatibility analysis
- Decision matrix creation
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 · 316 lines · 0 tokens per session scan A 94e1d7850162
intelligent-mcp-research-suite is a command published in the GitHub repository glassBead-tc/widescreen-research (6 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,233 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 2026-08-31.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.