szeider/consult7

**Consult7** is a Model Context Protocol (MCP) server that enables AI agents to consult large context window models via OpenRouter for analyzing extensive file collections - entire codebases, document repositories, or mixed content that exceed the current agent's context limits.
Why Consult7?
**Consult7** enables any MCP-compatible agent to offload file analysis to large context models (up to 2M tokens). Useful when:
- Agent's current context is full
- Task requires specialized model capabilities
- Need to analyze large codebases in a single query
- Want to compare results from different models
> "For Claude Code users, Consult7 is a game changer."
How it works
**Consult7** collects files from the specific paths you provide (with optional wildcards in filenames), assembles them into a single context, and sends them to a large context window model along with your query. The result is directly fed back to the agent you are working with.
Example Use Cases
Quick codebase summary
* **Files:** ["/Users/john/project/src/*.py", "/Users/john/project/lib/*.py"]
* **Query:** "Summarize the architecture and main components of this Python project"
* **Model:** "google/gemini-3.8-flash"
* **Mode:** "fast"
Deep analysis with reasoning
* **Files:** ["/Users/john/webapp/src/*.py", "/Users/john/webapp/auth/*.py", "/Users/john/webapp/api/*.js"]
* **Query:** "Analyze the authentication flow across this codebase. Think step by step about security vulnerabilities and suggest improvements"
* **Model:** "anthropic/claude-opus-4.8"
* **Mode:** "think"
Generate a report saved to file
* **Files:** ["/Users/john/project/src/*.py", "/Users/john/project/tests/*.py"]
* **Query:** "Generate a comprehensive code review report with architecture analysis, code quality assessment, and improvement recommendations"
* **Model:** "google/gemini-4-argon"
* **Mode:** "think"
* **Output File:** "/Users/john/reports/code_review.md"
* **Result:** Returns "Result has been saved to /Users/john/reports/code_review.md" plus a one-line metadata footer, instead of flooding the agent's context
Featured: Gemini Models
Consult7 supports **Google's Gemini** family:
- **Gemini 4 Argon** (google/gemini-4-argon) - Flagship reasoning model, 1M context
- **Gemini 3.8 Flash** (google/gemini-3.8-flash) - Fast and cheap, 1M context
**Quick mnemonics for power users:**
- **gemt** = Gemini 4 Argon + think (flagship reasoning)
- **gemf** = Gemini 3.8 Flash + fast (fast and cheap)
- **gptt** = GPT-6 Astra + think (latest GPT, effort xhigh)
- **grot** = Grok 4.7 + think (effort xhigh)
- **oput** = Claude Opus 4.8 + think (adaptive thinking)
- **fabt** = Claude Fable 5.1 + think (deepest reasoning, effort xhigh; premium)
- **ULTRA** = Run GPTT, GROT, and FABT in parallel (3 frontier models)
- **FUSE** = Fusion: a frontier panel deliberates and a judge synthesizes, in one call
These mnemonics make it easy to reference model+mode combinations in your queries.
> **Note on Fable 5.1.** anthropic/claude-fable-5.1 is Anthropic's most capable model but priced at a premium (~2× Opus 4.8). It **does not replace Opus 4.8** as the everyday Claude choice for single calls. Since v3.11.0 it holds the Anthropic seat in the ULTRA panel (which is meant for hard questions anyway). Unlike Opus 4.8 (adaptive thinking only), OpenRouter honors Fable's effort scale, so mid/think map to effort=high/effort=xhigh.
Featured: Fusion (multi-model analysis)
Consult7 supports OpenRouter's **Fusion** (openrouter/fusion) — a single call where a panel of frontier models (Opus, GPT, Gemini Pro) answers your query in parallel and a judge model synthesizes their responses into one answer. Reach for it on hard questions where multiple perspectives help and the cost of being wrong outweighs a few extra completions.
- **Context:** 128K — smaller than the 1M–2M single models, so it's best for hard questions on moderate input, not giant file bundles.
- **Mode → research depth:** fast / mid / think map the panel's web-search/fetch budget to max_tool_calls of 2 / 8 / 16.
- **Mnemonic:** FUSE = openrouter/fusion.
Trivial prompts answer directly (no panel); the panel fires only when the question warrants deliberation. Fusion is billed per panel run, so it costs more than a single-model call.