foldwork-dev/mcp-injector

AI coding assistants often fail because they retrieve the wrong context. On a large codebase, blindly dumping raw files into the prompt leads to hallucinations, slow responses, and high API costs.
Foldwork fixes this. It is a deterministic repository understanding engine that pre-indexes your entire codebase into a local SQLite catalog. It acts as the Context Layer for your IDE, serving exactly the functions the AI needs—no more, no less—maximizing the first-try success rate and reducing token usage by 41-89%.
By combining AST body folding (which strips out function bodies while preserving signatures) with canonical determinism (which guarantees byte-identical outputs to maximize Anthropic's KV cache hits), Foldwork transforms massive enterprise monorepos into lightweight, cache-friendly payloads. This drastically reduces token consumption, cuts API costs by up to 90%, and eliminates context window overflow.
No cloud. No telemetry. Runs entirely on your machine.--
Real-World Codebase Context Benchmarks
Estimate the impact of AST code compression on large open-source repositories (calculated at $2.00 / million input tokens for Claude Sonnet 5):
| Repository | Total Files | Raw Context Tokens | Compressed Context Tokens | Token Reduction | Cost Saved / Run |
| :--- | :---: | :---: | :---: | :---: | :---: |
| **Django** | 2,359 | 5,554,607 | 596,752 | **89.3%** | **$10.99** |
| **Tokio** | 789 | 1,597,813 | 444,164 | **72.2%** | **$3.11** |
| **Gin** | 99 | 197,300 | 47,718 | **75.8%** | **$0.39** |
*Numbers are reproducible. Run the open-source benchmark tool on any public repository:*
**mcp-benchmark repository**
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What It Looks Like
Run mcp-benchmark on your own project to see your exact savings before installing anything:
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Tools
get_project_map
Returns a compressed structural overview of the workspace. Function bodies are folded and replaced with placeholders to reduce token usage.
- tier (integer, optional): Compression tier to apply (default: 2).
- unfolded_files (array of strings, optional): Workspace-relative paths or glob patterns for files to serve at full resolution (uncompressed).
- path_prefixes (array of strings, optional): Scope the project map to specific microservices or packages, drastically reducing payload bloat.
- git_context: always includes current branch, changed files, and recent commits in the response.
- secrets_redacted: count of credentials automatically redacted before sending to Claude.
Example call:
injector_retrieve
Retrieves the full uncompressed source of a file from the local cache.
- path (string, required): The workspace-relative path of the file to retrieve.
- retrievalKey (string, optional): The SHA-256 retrieval key returned in a prior compressed payload.
- start_line (integer, optional): 1-indexed start line for range retrieval.
- end_line (integer, optional): 1-indexed end line for range retrieval.
- expand_graph (boolean, optional): Resolves and appends cross-file dependencies (limited to 50 1st-degree dependencies).
injector_search
BM25-ranked full-text symbol search over the local SQLite catalog. Supports FTS5 boolean logic (e.g., user AND (auth OR login)).
- query (string, required): FTS5 query string (bare terms, "phrase", prefix*).
- limit (integer, optional): Maximum results (default: 20).
- search_paths (array of strings, optional): Scope search to specific isolated directories.
injector_diagram
Generates a Mermaid sequence diagram for a given symbol by traversing its outbound dependencies (halts after 500 nodes).
- symbol (string, required): The exact symbol name.
- max_depth (integer, optional): Maximum traversal depth (default: 3).
- include_primitives (boolean, optional): Include basic types (String, boolean) and framework boundaries.
injector_regex_search
Fallback for exact literal or regex searches against file contents. Bypasses FTS5 tokenization.
- query (string, required): The string or regex pattern to search for.