Context Mode vs Golf: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Context Mode and Golf — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Context Mode
mksglu
An MCP server that sandboxes tool output, persists session memory in SQLite, and enforces context routing across 17 AI coding platforms.
Key features
- Sandboxed Tool Output: Tool results are executed and filtered outside the context window so only the answer returns to the model, cutting a 315 KB payload to about 5.4 KB.
- SQLite Session Continuity: File edits, git operations, tasks, errors, and user decisions are tracked in SQLite so the agent can resume exactly where it left off after a compaction.
- FTS5 + BM25 Retrieval: Session events are indexed into SQLite FTS5 and retrieved by BM25 relevance rather than being replayed wholesale into context.
- Think-in-Code Routing: A mandatory paradigm across supported clients that has the model write a script to compute results instead of reading files into context, replacing many tool calls with one.
- Hook-Based Enforcement: On Claude Code the plugin registers PreToolUse, PostToolUse, UserPromptSubmit, PreCompact, SessionStart, and Stop hooks so routing is injected at runtime with no file written to your project.
- Eleven MCP Tools: Six sandbox tools (ctx_batch_execute, ctx_execute, ctx_execute_file, ctx_index, ctx_search, ctx_fetch_and_index) plus five meta tools for stats, diagnostics, upgrade, purge, and insight.
- Persistent Knowledge Base: Local files and directories can be indexed into an FTS5 store and searched later, with a purge command to delete everything permanently.
- Savings Diagnostics: ctx_stats reports per-tool context savings and token consumption while ctx_doctor validates runtimes, hooks, FTS5, and plugin registration.
Best for
- Long Coding Sessions: Keep a multi-hour agent session usable by preventing large Playwright snapshots, issue lists, and logs from consuming the context window.
- Surviving Compaction: Resume work after the conversation compacts without the agent forgetting which files it was editing or what task was in progress.
- Large Log and Data Analysis: Have the agent script the analysis over a 45 KB access log rather than reading it into context to reason over it manually.
- Multi-Platform Agent Setups: Standardize context routing across Claude Code, Cursor, and the other supported clients so behavior is consistent regardless of tool.
- Cost Reduction: Cut token spend on agent-heavy workflows by removing raw tool payloads from prompts.
- Team Context Analytics: Use the hosted Insight dashboard to see how much context engineering teams are saving across projects.
Golf
golf.dev
Production-ready MCP server framework and firewall that protects MCP providers from prompt injections and PII leaks.
Key features
- MCP Firewall: Network and prompt-level protection that detects and blocks prompt-injection attempts and prevents PII leakage from agent conversations, reducing data-exposure risk for users.
- Python-Based Server Framework: Define tools, prompts, and resources as conventional Python files; Golf auto-discovers, parses, and compiles these components into a runnable MCP server to minimize boilerplate.
- Built-in Auth & Access Control: Integrated authentication and authorization primitives to manage user and agent permissions for secure production deployments.
- Observability & Telemetry: Runtime telemetry, logs, and metrics collection plus anonymous CLI usage telemetry to monitor MCP health, performance, and usage patterns for debugging and optimization.
- Debugger & Runtime Tools: Developer-facing debugger and runtime facilities to run, inspect, and iterate on MCP behavior and tool integrations during development and testing.
- Testing Framework (golf-testing): CLI tooling to test MCPs for performance, security, and compliance, enabling validation before production rollout.
- Production Readiness: Features targeted at enterprise deployments such as scalable runtime components, telemetry hooks, and security-first defaults to run real-world MCPs powering AI agents.
- MCP firewall layer to detect and block prompt injection attempts
- PII leak detection and protection for user data
- Production-ready MCP server framework implemented in Python
- Built-in authentication and authorization components
- Observability and telemetry integration for monitoring MCPs
- Runtime tooling and debugger for developing and troubleshooting MCPs
- Companion testing CLI/framework (golf-testing) for performance, security and compliance
Best for
- Building production MCP servers that power multi-component AI agents with defined tools, prompts, and resource bindings authored in Python.
- Protecting hosted MCP endpoints from prompt-injection attacks and preventing accidental leaks of PII or sensitive responses to users.
- Running pre-deployment security, performance, and compliance tests using the golf-testing framework to validate MCPs at scale.
- Integrating observability and telemetry into agent infrastructure to trace incidents, monitor usage, and optimize runtime performance.
- Rapid prototyping and iteration of agent capabilities via the file-based component model and local debugger/runtime before production deployment.
- Managing authentication and access control for enterprise MCP deployments to enforce permissioned use of tools and data by agents.
- Protect enterprise MCP deployments from prompt-injection attacks and accidental PII exposure
- Build and run production MCP servers that power AI agents with integrated Auth, Telemetry and Debugger
- Run automated security, performance and compliance tests against MCP implementations using the golf-testing tool
- Add observability and telemetry to MCP runtimes to monitor usage and troubleshoot agent behavior