budget-aware-mcp

Model-agnostic code memory MCP server. Budget-aware graph retrieval for AI agents — sub-millisecond queries, token budgeting, deterministic results. No embeddings, no vector DB, no API keys.

Built on CodeGraphContext for 155-language tree-sitter indexing. Replaces their retrieval layer with hop-based graph walks that respect token budgets.

Why this exists

Every other code MCP tool dumps context at you — "here's 50k tokens of everything I found." Your agent's context overflows, it hallucinates, or it wastes money processing irrelevant code.

budget-aware-mcp is different: the agent says "give me context for AuthService, max 8000 tokens" and gets exactly 8000 tokens of the most structurally-relevant code, walking outward from the anchor symbol hop by hop until the budget is hit. No waste. No overflow.

Install

npm install -g budget-aware-mcp
budget-aware-mcp install

Auto-detects and configures: Kiro, Claude Code, Cursor, VS Code, Windsurf, Zed, Codex CLI, Gemini CLI, Aider, OpenCode.

Or from source:

git clone https://github.com/Doorman11991/budget-aware-mcp.git
cd budget-aware-mcp
npm install && npm run build
budget-aware-mcp install

Performance

┌───────────────────────────────┬─────────┬─────────┐
│ Operation                     │ Avg(ms) │ P95(ms) │
├───────────────────────────────┼─────────┼─────────┤
│ Graph walk depth=2            │    0.07 │    0.11 │
│ Fuzzy search                  │    0.25 │    0.64 │
│ Explain symbol                │    8.00 │   12.00 │
│ Scope check                   │    0.04 │    0.48 │
│ Discover architecture         │    1.41 │    2.33 │
│ Index 108 files (41k LOC)     │  529.00 │  600.00 │
└───────────────────────────────┴─────────┴─────────┘

Queries are sub-millisecond once the server is warm. Semantic cache makes repeated/similar queries instant.

Tools (22)

Index

Tool	Description
index_repo	Parse files, build symbol graph, persist to SQLite. 155 languages via tree-sitter.
list_repos	List all indexed repositories with stats
get_repo_stats	Detailed stats: symbol kinds, languages, edge types, top connected symbols

Retrieval

Tool	Description
graph_walk	BFS from anchor symbol. Budget-aware, deterministic, hop-limited.
search_graph	Natural language → fuzzy match → graph walk. Semantic cache enabled.
check_scope	"Is this task feasible?" — answers without calling any LLM.
trace_call_path	Shortest path between two symbols via call/import edges.
analyze_impact	Blast radius: what breaks if these files change?

Discovery

Tool	Description
fuzzy_find_symbol	camelCase/snake_case splitting search. Semantic cache enabled.
find_by_path	Search files by path pattern
find_by_signature	"Something that takes User and returns Token"
discover_subsystems	Architecture overview: clusters, hotspots, entry points, languages
find_similar	Structural similarity without embeddings
expand_neighborhood	Hop=1 from a symbol — bridge into the full graph

Context

Tool	Description
get_file_context	File + all its dependencies, within token budget
explain_symbol	One-shot: signature, callers, callees, location, connectivity
suggest_files	"What files should I look at for this task?" — ranked by relevance
find_dead_code	Symbols with zero inbound edges (nothing calls them)
get_code_snippet	Read actual source code with line numbers
search_code	Full-text regex search across file contents with context

Management

Tool	Description
delete_project	Remove an indexed repository from the graph
get_session_stats	Cumulative token accounting across all queries

Key Features (unique to budget-aware-mcp)

These features don't exist in CodeGraphContext or any other code MCP server. CodeGraphContext is a required dependency for full 155-language tree-sitter indexing — we build our retrieval layer on top of their index.

---

1. Token Budget Per Query

Problem: Other tools return everything they find. An agent asks "what's relevant to auth?" and gets 50,000 tokens of code. Most of it is irrelevant. The agent's context window overflows or it wastes money processing junk.

Our solution: Every query has a max_tokens parameter. The graph walk starts at your anchor symbol, walks outward along real call/import edges, and stops adding context the moment the budget is hit.

Agent: graph_walk("AuthService", hop_depth=2, max_tokens=8000)

Result: 20 symbols across 12 files, exactly 7,998 tokens.
        Stopped at hop 2 because adding the next symbol would exceed 8000.

The agent asked for 8000. It got 8000. Not 50,000. Not 3,000. Exactly what it asked for, filled with the most structurally-connected code first.

---

2. Token Accounting Per Session

Problem: Agents have no idea how much context they've consumed across a conversation. After 10 queries they might have blown through 100k tokens without realizing it.

Our solution: Every query is tracked. The agent can ask at any time: "how much have I spent?"

Agent: get_session_stats()

Result:
  total_queries: 8
  total_tokens_returned: 24,500
  repos_indexed: 2
  coverage: 12% of codebase explored

This lets agents make smart decisions: "I've already seen 24k tokens — do I need more context or should I start generating?" Instead of blindly reading more files, the agent knows its budget.

---

3. Scope/Feasibility Check

Problem: An agent decides to "refactor the PaymentProcessor class." It generates 200 lines of code referencing PaymentProcessor, StripeAdapter, and WebhookHandler. None of those exist in the codebase. The agent hallucinated the entire thing.

Our solution: Before generating code, the agent asks "is this task doable?"

Agent: check_scope("refactor PaymentProcessor to support Stripe webhooks")

Result:
  feasibility: "unknown"
  found_symbols: []
  missing_symbols: ["PaymentProcessor", "Stripe", "webhook"]
  confidence: 0.1

→ Agent knows: these symbols don't exist. Don't generate code for them.

vs.

Agent: check_scope("refactor the Emitter class to support multiple targets")

Result:
  feasibility: "full"
  found_symbols: ["Emitter"]
  confidence: 0.95

→ Agent knows: Emitter exists, go ahead.

Zero LLM calls. Pure graph lookup. Prevents wasted generation attempts.

---

4. Blast-Radius Impact Analysis

Problem: Other tools detect "these files changed" (git diff). That's not useful for an agent planning a refactor — it needs to know "if I change THIS, what ELSE breaks?"

Our solution: Given a list of changed files, walks the dependency graph backwards to find everything that depends on the changed code.

Agent: analyze_impact(changed_files=["auth.ts"], hop_depth=2)

Result:
  changed_symbols: ["AuthService", "validateToken", "refreshSession"]
  blast_radius: 14 symbols across 8 files depend on these
  affected_files: ["routes/user.ts", "middleware/auth.ts", "services/payment.ts", ...]

The agent now knows: "If I change auth.ts, I might break 8 other files. Let me check those too before I submit this PR."

---

5. Deterministic Ordering

Problem: BM25 search scores change based on index state, document frequency, and other factors. The same query returns different results on different days. You can't reproduce a bug, you can't write reliable tests, you can't trust the output.

Our solution: Same query, same result. Every time. No exceptions.

How: within each hop level of the graph walk, symbols are sorted alphabetically by FQN. The walk is BFS (breadth-first), so hop 0 is always the anchor, hop 1 is always its direct connections sorted A-Z, hop 2 is always THEIR connections sorted A-Z.

Run 1: graph_walk("Emitter", 2, 8000) → [Emitter, emitBudget, emitCache, emitCheckpoint, ...]
Run 2: graph_walk("Emitter", 2, 8000) → [Emitter, emitBudget, emitCache, emitCheckpoint, ...]
Run 3: graph_walk("Emitter", 2, 8000) → [Emitter, emitBudget, emitCache, emitCheckpoint, ...]

Always the same. Debuggable. Reproducible. Testable.

---

6. Multi-Hop Walk With Budget Cutoff

Problem: A flat search returns "here are 20 results ranked by keyword relevance." But code isn't flat — it's a graph. AuthService calls TokenValidator which calls CryptoUtils. You need to understand the CHAIN, not just individual matches.

Our solution: Start at a symbol. Walk outward along actual call/import/inheritance edges. Each "hop" adds the next layer of connected code. Stop when the budget is full.

Hop 0: AuthService (the thing you asked about)
Hop 1: validateToken, refreshSession, hashPassword (things AuthService calls)
Hop 2: CryptoUtils, SessionStore, TokenBlacklist (things THOSE call)
        ↑ stopped here — budget hit at 8000 tokens

This gives the agent a connected subgraph — not a flat list. It understands the call chain, the dependencies, the architecture. In 8000 tokens instead of reading 12 files manually (50,000+ tokens).

---

Dependency: CodeGraphContext

budget-aware-mcp uses CodeGraphContext as its indexing engine. When installed, it provides:

• 155-language tree-sitter parsing
• 4000+ edges per project (calls, imports, inheritance, usage)
• Incremental re-indexing
• 3D graph visualization (localhost:9749)

Install it for best results:

# Windows
powershell -c "irm https://raw.githubusercontent.com/CodeGraphContext/CodeGraphContext/main/install.ps1 | iex"

# macOS/Linux
curl -fsSL https://raw.githubusercontent.com/CodeGraphContext/CodeGraphContext/main/install.sh | bash

Without CodeGraphContext, budget-aware-mcp falls back to its built-in regex parser (~30 languages, 86% edge coverage). Everything still works — just fewer edges to walk.

• Entry points: functions with high out-degree but low in-degree
• Language breakdown: files and LOC per language

How it differs from CodeGraphContext

	CodeGraphContext	budget-aware-mcp
Retrieval	BM25 keyword search	Hop-based graph walk with token budget
Token control	None — returns everything	Agent specifies max, retrieval stops there
Determinism	BM25 scores vary	Same query = same result, always
Caching	None	Semantic cache (similar queries → instant)
Scope check	Not available	"Is this task feasible?"
Explain symbol	Not available	One call: signature + callers + callees
File context	Not available	File + all dependencies in one shot
Suggest files	Not available	Task → ranked file list
Dead code	Not available	Zero-inbound-edge detection
Session tracking	Not available	Cumulative token spend
Code reading	get_code_snippet	get_code_snippet + search_code
Architecture	Packages, layers, routes	Clusters, hotspots, entry points
Startup	~15ms (native C)	~200ms (Node.js) — 0.07ms per query once warm

Architecture

AI Agent (any MCP client)
  ↓ stdio (JSON-RPC 2.0)
budget-aware-mcp
  ├── Semantic Cache (trigram similarity, 5min TTL)
  ├── Retrieval (graph_walk, fuzzy, scope_check, cluster, similarity)
  ├── Context (get_file_context, explain_symbol, suggest_files)
  ├── Code Access (get_code_snippet, search_code)
  ├── CodeGraphContext .db (when installed — 155 langs, 4000+ edges)
  ├── Built-in parser (regex + call resolution — 30 langs, 3500+ edges)
  └── SQLite (.code-graph/graph.db)

CLI

budget-aware-mcp              # Run MCP server on stdio
budget-aware-mcp install      # Auto-detect agents, configure MCP
budget-aware-mcp uninstall    # Remove MCP config from all agents
budget-aware-mcp --version    # Show version
budget-aware-mcp --help       # Show help

Development

npm test              # Run 19-test suite
npm run bench         # In-process latency benchmarks
npm run bench:compare # Side-by-side with CodeGraphContext

License

MIT