The Context Window Problem Is Real

If you’ve worked with AI coding agents, you’ve experienced it: your agent slows down, token costs spike, or tasks fail because the context window hit its limit. A recent article highlighted this pain point, showing that just three popular MCP servers consumed 26% of a coding agent’s context window.

The culprit? MCP servers that pre-load dozens of tool definitions into the context window whether the agent needs them or not. Some memory solutions expose 40+ tools, each with verbose descriptions that compound into thousands of tokens before your agent even starts working.

This is a legitimate concern. But the solution isn’t to abandon MCP entirely—it’s to design MCP servers with context efficiency as a first-class requirement.

MemoryGraph’s Approach: Judicious Tool Design

MemoryGraph takes a different path. Instead of offering every conceivable memory operation as a separate tool, we designed around a core principle: minimum tools, maximum capability.

The Numbers

Solution	Default Tools	Typical Context Usage
Heavy MCP servers	40+ tools	20-30% of context
MemoryGraph Core	9 tools	~2-3% of context
MemoryGraph Extended	11 tools	~3-4% of context

Nine tools. That’s it for 95% of use cases. And each tool description is crafted to be concise while remaining discoverable.

Tool Profiles: Context When You Need It

We implemented tool profiles to give users explicit control over their context footprint:

# Core mode (default) - 9 tools, minimal context
memorygraph

# Extended mode - 11 tools, adds statistics and advanced queries
memorygraph --profile extended

Most users never need extended mode. The core profile provides:

• Memory CRUD: store, get, update, delete, search (5 tools)
• Relationships: create links, traverse graph (2 tools)
• Discovery: fuzzy recall, session briefings (2 tools)

That covers storing solutions, linking problems to fixes, recalling past work, and catching up on project context. Extended mode adds database statistics and complex relationship queries—useful for power users, but users have to opt-in.

Why We Didn’t Abandon MCP

Some memory vendors have moved from MCP to CLI interfaces, arguing that agents are "natively fluent" in shell commands. While there’s merit to this argument, we believe it conflates two separate concerns:

1. The Problem Isn’t MCP—It’s Tool Sprawl

MCP itself is a thin protocol. The context cost comes from tool definitions, not the protocol. A well-designed MCP server with 9 concise tools uses far less context than a CLI wrapper with verbose --help output that gets loaded anyway.

2. CLI Loses MCP’s Ecosystem Benefits

MCP provides:

• Standardized tool discovery across clients (Claude Code, Cursor, VS Code Copilot, etc.)
• Consistent installation and configuration
• Client-managed tool execution and error handling
• Cross-platform support without wrapper scripts

Moving to CLI means maintaining separate integrations for each coding agent, handling authentication differently per environment, and losing the growing MCP ecosystem.

3. Graph Relationships Are Our Value Prop

A CLI interface forces flat, document-style storage. MemoryGraph’s power comes from typed relationships:

[timeout_fix] --CAUSES--> [memory_leak] --SOLVED_BY--> [connection_pooling]

Query: "What happened with retry logic?" returns the full causal chain—something flat storage can’t provide efficiently.

Concise Tool Descriptions: How We Stay Lean

Here’s an example of how we approach tool descriptions. Compare a verbose approach:

# Verbose (typical)
recall_memories: This is the recommended starting point for recalling past
memories and learnings from your knowledge graph. This tool wraps search_memories with optimal defaults for natural language queries. When you want to search for past work, solutions, problems, patterns, or project context, use this tool first.
It automatically uses fuzzy matching which handles plurals, tenses, and case
variations. Results always include relationship context showing what connects to what. This is simpler than search_memories for common use cases because it has optimized default settings applied. Pass a natural language query and optionally filter by memory types or project path. Results are ranked by relevance with match quality hints included.

Versus our actual approach:

# Concise (MemoryGraph)
recall_memories: Search memories with fuzzy matching and relationship context.
Best starting point for "What did we learn about X?" queries. Handles plurals and tenses automatically.

Same capability, fraction of the tokens.

What This Means in Practice

When you add MemoryGraph to Claude Code:

claude mcp add --scope user memorygraph -- memorygraph

Your agent gets persistent memory with graph relationships while consuming roughly 2-3% of context—leaving the rest for your actual work.

Compare that to solutions that consume 20%+ before you’ve even asked a question.

Our Commitment

We’re adding context footprint tracking to our documentation and website. Users should know exactly how much context each MCP server costs before they install it.

Upcoming improvements:

• Published context token counts per tool profile
• Tool description audit to minimize verbosity
• Continued focus on "minimum tools, maximum capability"

Conclusion

The context window problem is real, but MCP isn’t the enemy. Tool sprawl is. MemoryGraph proves you can have powerful graph-based memory with relationship tracking while staying context-efficient.

Nine tools. Graph relationships. 2-3% context usage.

That’s the balance we’ve found.

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Get started:

pipx install memorygraphMCP
claude mcp add --scope user memorygraph -- memorygraph

GitHub | Documentation

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MemoryGraph is an open-source MCP memory server for AI coding agents. We believe context efficiency and powerful features aren’t mutually exclusive.