Using The Force MCP Server

The Force provides unified access to 11 cutting-edge AI models through a single consistent interface with intelligent context management, long-term project memory, and multi-model collaboration capabilities.

Quick Reference: Model Selection

For 90% of work, use these two models:

Need	Model	Why
Default choice	chat_with_gpt52_pro	Smartest reasoning, 400k context, best at search
Large context	chat_with_gemini3_pro_preview	1M context, fast, excellent code analysis

Full Model Roster

Model	Context	Speed	Best For
OpenAI
chat_with_gpt52_pro	400k	Medium	Complex reasoning, code generation, search
chat_with_gpt52_pro_max	400k	Slow	24+ hour tasks, xhigh reasoning, 77.9% SWE-bench
chat_with_gpt41	1M	Fast	Large docs, RAG, low hallucination
research_with_o3_deep_research	200k	10-60 min	Exhaustive research with citations
research_with_o4_mini_deep_research	200k	2-10 min	Quick research reconnaissance
Google
chat_with_gemini3_pro_preview	1M	Medium	Giant code synthesis, design reviews
chat_with_gemini3_flash_preview	1M	Very fast	Quick summaries, extraction, triage
Anthropic
chat_with_claude45_opus	200k	Slow	Deep extended thinking, premium quality
chat_with_claude45_sonnet	1M	Fast	Latest Claude, strong coding
chat_with_claude3_opus	200k	Slow	Thoughtful writing, low hallucination
xAI
chat_with_grok41	~2M	Medium	Massive context, Live Search (X/Twitter)

Core Concepts

1. Context Management

The Force automatically handles large codebases by splitting between inline content and vector stores.

How it works:

1. Calculate token budget (85% of model's context window)
2. Sort files smallest-first to maximize complete files inline
3. Fill budget with files directly in prompt
4. Remaining files → searchable vector store
5. Cache this "stable list" for subsequent calls

Key parameters:

context: ["/abs/path/to/src", "/abs/path/to/docs"]
  - Files/directories to include
  - MUST be absolute paths
  - Auto-splits between inline and vector store

priority_context: ["/abs/path/to/critical/config.py"]
  - Files that MUST be inline regardless of size
  - Use sparingly for security configs, schemas
  - Still respects total budget

Rules:

• Always use absolute paths (relative paths resolve to server's CWD)
• Size limits: 500KB per file, 50MB total
• Respects .gitignore, skips binaries
• 60+ text file types supported

2. Session Management

Sessions enable multi-turn conversations with memory persistence.

session_id: "jwt-auth-refactor-2024-12-10"

Best practices:

• One session per logical thread of reasoning
• Use descriptive IDs: debug-race-condition-2024-12-10
• Reuse same session_id for follow-ups (conversation continues)
• Sessions work across models (switch models mid-conversation)
• Default TTL: 6 months

Session strategy:

# First call: Creates session
chat_with_gpt52_pro(
    instructions="Analyze auth flow",
    context=["/src/auth"],
    session_id="auth-analysis"
)

# Follow-up: Leverages prior context
chat_with_gpt52_pro(
    instructions="Now focus on the JWT validation",
    session_id="auth-analysis"  # Same session = remembers context
)

# Switch models, same session
chat_with_gemini3_pro_preview(
    instructions="Review what we found for security issues",
    session_id="auth-analysis"  # Works across models
)

3. Structured Output

Force models to return valid JSON matching a schema:

structured_output_schema: {
    "type": "object",
    "properties": {
        "issues": {"type": "array", "items": {"type": "string"}},
        "severity": {"type": "string", "enum": ["low", "medium", "high"]}
    },
    "required": ["issues", "severity"],
    "additionalProperties": false
}

Supported by most models. OpenAI requires strict schema (all props in required, additionalProperties: false).

4. Reasoning Effort

Control depth of model thinking (where supported):

Level	Use Case	Models
low	Quick answers	o3, o4-mini, gpt51-codex
medium	Balanced (default)	o3, o4-mini, gpt51-codex
high	Deep analysis	o3, o3-pro, gpt51-codex
xhigh	Maximum depth	gpt51-codex-max only

reasoning_effort: "high"  # For complex problems

Utility Tools

Project History Search

Search past conversations AND git commits:

search_project_history(
    query="JWT authentication decisions; refresh token strategy",
    max_results=20,
    store_types=["conversation", "commit"]
)

Important: Returns HISTORICAL data that may be outdated. Use to understand past decisions, not current code state.

Session Management

list_sessions(limit=10, include_summary=true)
describe_session(session_id="auth-analysis")

Token Counting

Estimate context size before sending:

count_project_tokens(
    items=["/src", "/tests"],
    top_n=10  # Show top 10 largest files
)

Async Jobs

For operations >60s (deep research, large token counts):

# Start background job
job = start_job(
    target_tool="research_with_o3_deep_research",
    args={"instructions": "...", "session_id": "..."},
    max_runtime_s=3600
)

# Poll until complete
result = poll_job(job_id=job["job_id"])
# status: pending | running | completed | failed | cancelled

# Cancel if needed
cancel_job(job_id=job["job_id"])

Multi-Model Collaboration (GroupThink)

Orchestrate multiple models on complex problems:

group_think(
    session_id="design-auth-system",
    objective="Design zero-downtime auth service with JWT rotation",
    models=[
        "chat_with_gpt52_pro",         # Best reasoning
        "chat_with_gemini3_pro_preview", # Large context analysis
        "chat_with_claude45_opus"        # Design documentation
    ],
    output_format="Design doc with: Architecture, API endpoints, Migration plan",
    context=["/src/auth"],
    priority_context=["/docs/security-requirements.md"],
    discussion_turns=6,
    validation_rounds=2
)

How it works:

1. Discussion phase: Models take turns contributing to shared whiteboard
2. Synthesis phase: Large-context model creates final deliverable
3. Validation phase: Original models review and critique

Key parameters:

Parameter	Purpose	Default
session_id	Panel identifier (reuse to continue)	Required
objective	Problem to solve	Required
models	List of model tool names	Required
output_format	Deliverable specification	Required
discussion_turns	Back-and-forth rounds	6
validation_rounds	Review iterations	2
synthesis_model	Model for final synthesis	gemini3_pro_preview
direct_context	Inject history directly (vs vector search)	true

Continue existing panel:

group_think(
    session_id="design-auth-system",  # Same ID
    user_input="Now focus on the migration path",
    models=[...],  # Can be same or different
    objective="...",
    output_format="..."
)

Three-Phase Intelligence Gathering

Optimal pattern for complex analysis:

Phase 1: Broad Surface Scan (5-10s)

# Launch 2-3 fast queries in parallel
chat_with_gemini3_flash_preview(
    instructions="What are the main issues here?",
    context=["/src"],
    session_id="analysis-phase1-issues"
)

chat_with_gemini3_flash_preview(
    instructions="What patterns exist in this codebase?",
    context=["/src"],
    session_id="analysis-phase1-patterns"
)

Phase 2: Deep Focus (30-60s)

# Pursue top insights from Phase 1
chat_with_gpt52_pro(
    instructions="Deep analysis of [specific issue from Phase 1]",
    context=["/src"],
    session_id="analysis-phase2-deep"
)

Phase 3: Synthesis (10s)

# Reconcile findings
chat_with_gemini3_flash_preview(
    instructions="Synthesize these findings: [Phase 1 + Phase 2 results]",
    session_id="analysis-phase3-synthesis"
)

Model Selection Strategies

For Debugging

# Fast hypothesis generation
chat_with_gemini3_flash_preview("What could cause this error?", context=[...])

# Deep reasoning on top hypothesis
chat_with_gpt52_pro("Trace execution of [hypothesis]", context=[...])

# Validation from different perspective
chat_with_gemini3_pro_preview("What did we miss?", context=[...])

For Architecture Review

# Overview with large context
chat_with_gpt41(context=["/entire/codebase"], instructions="Find inconsistencies")

# Deep dive on subsystems
chat_with_gemini3_pro_preview("Analyze data layer for ACID issues")

# External research
research_with_o3_deep_research("Industry standards for [patterns found]")

For Code Generation

# Planning
chat_with_gpt52_pro("Design API structure for [requirements]")

# Implementation
chat_with_gemini3_pro_preview("Implement [design] with error handling")

# Review
chat_with_gpt41("Review for security and performance")

Error Handling

Rate Limits (429)

• Stagger launches by 100ms
• Use async jobs for high concurrency
• Fallback to cheaper models

Context Overflow

• Run count_project_tokens first
• Switch to larger-context model (gpt41, gemini3_pro)
• Use priority_context for must-includes
• Let server handle vector store split

Timeouts

• Offload to start_job / poll_job
• Use faster models for initial scan
• Break into smaller queries

Configuration

Environment Variables

OPENAI_API_KEY="sk-..."
GEMINI_API_KEY="..."
XAI_API_KEY="xai-..."
ANTHROPIC_API_KEY="sk-ant-..."
VERTEX_PROJECT="my-project"
VERTEX_LOCATION="us-central1"

Key Settings (config.yaml)

mcp:
  context_percentage: 0.85  # % of model context to use
  default_temperature: 0.2

session:
  ttl_seconds: 15552000  # 6 months

history:
  enabled: true

Best Practices Summary

1. Start Fast, Go Deep: Use gemini3_flash_preview for exploration, then targeted deep models
2. Parallel Everything: Launch multiple models simultaneously when possible
3. Session Hygiene: One session per logical thread of reasoning
4. Smart Fallbacks: Always have a faster/cheaper model as backup
5. Absolute Paths Only: Never use relative paths in context
6. Reuse Sessions: Save tokens by continuing conversations
7. Priority Context Sparingly: Only for truly critical files
8. Check History First: Search project history before making decisions
9. Monitor Tokens: Use count_project_tokens for large contexts
10. Async for Long Tasks: Use job system for >60s operations

Resources

This skill includes quick-reference materials in references/:

• model-selection-guide.md: Detailed model comparison and selection criteria
• common-patterns.md: Copy-paste patterns for common workflows