Analyze Codebases with Large Context Models
Consult7 MCP Server: AI agents analyze large codebases and files using OpenRouter models up to 2M tokens.
Why it matters
Leverage large context window AI models via OpenRouter to analyze extensive codebases and document repositories that exceed standard agent context limits.
Outcomes
What it gets done
Analyze entire codebases in a single request.
Consult with large context models for file analysis.
Generate code review reports and security suggestions.
Summarize project architecture and main components.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-consult7 | bash Capabilities
Tools your agent gets
Consults with large context models to analyze files with a specified request, model, and reasoning mode
Overview
consult7 MCP Server
What it does
Consult7 is an MCP server that allows AI agents to interact with large context window models through OpenRouter. It enables the analysis of extensive file collections, entire codebases, and document repositories that exceed an AI agent's typical context limits. The server supports over 500 models and offers various performance modes.
How it connects
Use Consult7 when you need to analyze codebases or document repositories that are too large for your AI agent's standard context window. It is ideal for tasks such as comprehensive code reviews, architectural analysis, security audits, and deep dives into extensive documentation.
Source README
Consult7 MCP Server
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-flash-preview" - 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-2.5-pro" - Mode:
"think" - Output File:
"/Users/john/reports/code_review.md" - Result: Returns
"Result has been saved to /Users/john/reports/code_review.md"instead of flooding the agent's context
Featured: Gemini 3.1 Models
Consult7 supports Google's Gemini 3.1 family:
- Gemini 3.1 Pro (
google/gemini-3.1-pro-preview) - Flagship reasoning model, 1M context - Gemini 3 Flash (
google/gemini-3-flash-preview) - Ultra-fast model, 1M context - Gemini 3.1 Flash Lite (
google/gemini-3.1-flash-lite-preview) - Ultra-fast lite model, 1M context
Quick mnemonics for power users:
gemt= Gemini 3.1 Pro + think (flagship reasoning)gemf= Gemini 3 Flash + fast (ultra fast)gptt= GPT-5.5 + think (latest GPT)grot= Grok 4.20 + think (automatic reasoning)oput= Claude Opus 4.8 + think (adaptive thinking)ULTRA= Run GEMT, GPTT, GROT, and OPUT in parallel (4 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.
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/thinkmap the panel's web-search/fetch budget tomax_tool_callsof 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.
Installation
Claude Code
Simply run:
claude mcp add -s user consult7 uvx -- consult7 your-openrouter-api-key
Claude Desktop
Add to your Claude Desktop configuration file:
{
"mcpServers": {
"consult7": {
"type": "stdio",
"command": "uvx",
"args": ["consult7", "your-openrouter-api-key"]
}
}
}
Replace your-openrouter-api-key with your actual OpenRouter API key.
No installation required - uvx automatically downloads and runs consult7 in an isolated environment.
Command Line Options
uvx consult7 <api-key> [--test]
<api-key>: Required. Your OpenRouter API key--test: Optional. Test the API connection
The model and mode are specified when calling the tool, not at startup.
Supported Models
Consult7 supports all 500+ models available on OpenRouter. Below are the flagship models with optimized dynamic file size limits:
| Model | Context | Use Case |
|---|---|---|
openai/gpt-5.5 |
1M | Latest GPT, balanced performance |
google/gemini-3.1-pro-preview |
1M | Flagship reasoning model |
google/gemini-3-flash-preview |
1M | Gemini 3 Flash, ultra fast |
google/gemini-3.1-flash-lite-preview |
1M | Ultra-fast lite model |
anthropic/claude-opus-4.8 |
1M | Best quality, adaptive thinking |
anthropic/claude-sonnet-4.6 |
1M | Excellent reasoning, fast |
anthropic/claude-haiku-4.5 |
200k | Budget, very fast |
x-ai/grok-4.20 |
2M | Automatic reasoning, huge context |
x-ai/grok-4.1-fast |
2M | Largest context window |
openrouter/fusion |
128k | Multi-model panel + judge (see Featured: Fusion) |
Quick mnemonics:
gptt=openai/gpt-5.5+think(latest GPT, deep reasoning)gemt=google/gemini-3.1-pro-preview+think(Gemini 3.1 Pro, flagship reasoning)grot=x-ai/grok-4.20+think(Grok 4.20, automatic reasoning)oput=anthropic/claude-opus-4.8+think(Claude Opus, adaptive thinking)opuf=anthropic/claude-opus-4.8+fast(Claude Opus, no reasoning)gemf=google/gemini-3-flash-preview+fast(Gemini 3 Flash, ultra fast)ULTRA= call GEMT, GPTT, GROT, and OPUT IN PARALLEL (4 frontier models for maximum insight)FUSE=openrouter/fusion(one call: a frontier panel deliberates, a judge synthesizes; mode sets web-research depth)
You can use any OpenRouter model ID (e.g., deepseek/deepseek-r1-0528). See the full model list. File size limits are automatically calculated based on each model's context window.
Performance Modes
fast: No reasoning - quick answers, simple tasksmid: Moderate reasoning - code reviews, bug analysisthink: Maximum reasoning - security audits, complex refactoring
File Specification Rules
- Absolute paths only:
/Users/john/project/src/*.py - Wildcards in filenames only:
/Users/john/project/*.py(not in directory paths) - Extension required with wildcards:
*.pynot* - Mix files and patterns:
["/path/src/*.py", "/path/README.md", "/path/tests/*_test.py"]
Common patterns:
- All Python files:
/path/to/dir/*.py - Test files:
/path/to/tests/*_test.pyor/path/to/tests/test_*.py - Multiple extensions:
["/path/*.js", "/path/*.ts"]
Automatically ignored: __pycache__, .env, secrets.py, .DS_Store, .git, node_modules
Size limits: Dynamic based on model context window (e.g., Grok 4.20: ~8MB, GPT-5.5: ~4MB)
Tool Parameters
The consultation tool accepts the following parameters:
- files (required): List of absolute file paths or patterns with wildcards in filenames only
- query (required): Your question or instruction for the LLM to process the files
- model (required): The LLM model to use (see Supported Models above)
- mode (required): Performance mode -
fast,mid, orthink - output_file (optional): Absolute path to save the response to a file instead of returning it
- If the file exists, it will be saved with
_updatedsuffix (e.g.,report.md→report_updated.md) - When specified, returns only:
"Result has been saved to /path/to/file" - Useful for generating reports, documentation, or analyses without flooding the agent's context
- If the file exists, it will be saved with
- zdr (optional): Enable Zero Data Retention routing (default:
false)- When
true, routes only to endpoints with ZDR policy (prompts not retained by provider) - ZDR available: Gemini 3.1 Pro/Flash, Claude Opus 4.8, GPT-5, GPT-5.5
- Not available: Grok 4.20 (returns error)
- When
Usage Examples
Via MCP in Claude Code
Claude Code will automatically use the tool with proper parameters:
{
"files": ["/Users/john/project/src/*.py"],
"query": "Explain the main architecture",
"model": "google/gemini-3-flash-preview",
"mode": "fast"
}
Via Python API
from consult7.consultation import consultation_impl
result = await consultation_impl(
files=["/path/to/file.py"],
query="Explain this code",
model="google/gemini-3-flash-preview",
mode="fast", # fast, mid, or think
provider="openrouter",
api_key="sk-or-v1-..."
)
Testing
# Test OpenRouter connection
uvx consult7 sk-or-v1-your-api-key --test
Uninstalling
To remove consult7 from Claude Code:
claude mcp remove consult7 -s user
Version History
v3.7.1
- Surface mid-stream API errors: when OpenRouter sends an error as a streaming data chunk (after the initial 200), the call now returns that error message instead of a misleading "No content received".
v3.7.0
- Added Fusion (
openrouter/fusion) - a multi-model panel plus a judge in one call;modemaps to web-research depth (fast/mid/think→max_tool_calls2/8/16). NewFUSEmnemonic. - Upgraded Claude Opus 4.7 → 4.8 (1M context, adaptive thinking);
oput/opufnow point to 4.8, and 4.7 is kept as a legacy ID. - The response footer now reports the call cost in USD (from OpenRouter usage accounting), e.g.
cost: $0.0923.
v3.6.1
- Toggle-reasoning footer now distinguishes
midvsthinkfor adaptive models (Opus, Grok) - Friendlier error message when a model has no Zero Data Retention endpoint
output_filereturn now includes the metadata footer so callers can verify what ran
v3.6.0
- Upgraded models: GPT-5.5, Claude Opus 4.7, Grok 4.20
- Claude Opus 4.7 (1M context) uses adaptive thinking -
reasoning.enabled=true - Grok 4.20 (2M context) uses automatic reasoning -
reasoning.enabled=true - Updated mnemonics:
gptt→ GPT-5.5,oput/opuf→ Claude Opus 4.7,grot→ Grok 4.20 - Legacy model IDs still supported
v3.5.0
- Upgraded GPT-5.2 → GPT-5.4 (~1M context)
v3.4.0
- Upgraded models: Gemini 3.1 Pro, Claude Opus 4.6, Claude Sonnet 4.6, Grok 4.1 Fast
- Added new models: Claude Haiku 4.5, Gemini 3.1 Flash Lite
- Updated mnemonics:
gemt→ Gemini 3.1 Pro,oput/opuf→ Claude Opus 4.6 - Legacy model IDs still supported
v3.3.0
- Fixed GPT-5.2 thinking mode truncation issue (switched to streaming)
- Added
google/gemini-3-flash-preview(Gemini 3 Flash, ultra fast) - Updated
gemfmnemonic to use Gemini 3 Flash - Added
zdrparameter for Zero Data Retention routing
v3.2.0
- Updated to GPT-5.2 with effort-based reasoning
v3.1.0
- Added
google/gemini-3-pro-preview(1M context, flagship reasoning model) - New mnemonics:
gemt(Gemini 3 Pro),grot(Grok 4),ULTRA(parallel execution)
v3.0.0
- Removed Google and OpenAI direct providers - now OpenRouter only
- Removed
|thinkingsuffix - usemodeparameter instead (now required) - Clean
modeparameter API:fast,mid,think - Simplified CLI from
consult7 <provider> <key>toconsult7 <key> - Better MCP integration with enum validation for modes
- Dynamic file size limits based on model context window
v2.1.0
- Added
output_fileparameter to save responses to files
v2.0.0
- New file list interface with simplified validation
- Reduced file size limits to realistic values
Discussion
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