toolrecall 0.6.0

The Deterministic Tool Cache for LLM Agents — no LLM decides what to cache. SQLite FTS5, zero deps, MCP multiplexer, zero-trust WAF.

ToolRecall — Deterministic Tool Cache for LLM Agents

ToolRecall sits between your agent and the OS. On repeat calls it serves cached results from local SQLite instead of re-executing system commands. Caching is deterministic — byte-identical until mtime/TTL expiry — which qualifies every API call for provider prefix-caching discounts (up to 90% at Anthropic/OpenAI).

1 tick instead of 4: A file read normally needs stat → open → read → close. ToolRecall needs only stat (mtime check) — on cache hit the bytes come from memory, bypassing disk entirely.

Zero pip dependencies. Python 3.11+ stdlib only. 76 KB install. One daemon.

pip install toolrecall    # Installs nothing but ToolRecall itself
toolrecall init            # Interactive security setup (default-deny paths)
toolrecall daemon &         # Start cache daemon
toolrecall mcp              # Connect any MCP agent (Claude Code, Cursor, Cline, Hermes...)

Requirements: Python 3.11+ (sqlite3, tomllib, json, http.server, urllib from stdlib).

---

What It Does

ToolRecall intercepts tool calls at the daemon level and returns cached results when inputs haven't changed:

Mechanism	What gets cached	Invalidation
File cache	First disk read per file	mtime changes → fresh read
Terminal cache	Static commands (hostname, whoami, pwd, uname, uptime, df, free, crontab)	TTL-based (default 300s)
MCP cache	External MCP server responses (GitHub, time, fetch...)	TTL-based (default 60s, per-server override)
Script/Code cache	cached_run, cached_exec output	ttl=0 disables caching

Dynamic commands (git, ls, curl) and state-changing operations always execute live.

Measured effect

In a 13-hour session (Hermes + Gemini 3.1 Pro, 386 messages, 13 project files):

• 89% hit rate (91% file cache): 827 tool calls served from SQLite instead of OS
• 73% fewer file-read tokens at 3× re-read (~204K → ~55K unique)
• ~81% fewer at 10× re-read (~630K → ~55K unique)
• ~20 min less wait time — each cache hit avoids ~1.5s subprocess fork
• Provider prefix-caching becomes reliable: byte-identical payloads qualify for Anthropic/OpenAI's up-to-90% discount on every call

Source: Benchmark

---

Architecture

  [ Claude Code ]   [ Cursor IDE ]   [ Hermes Agent ]
         \                |                /
          \               |               /
        +───────────────────────────────────+
        │  Standard stdio Protocol (Bridge) │  <- Client Layer
        +─────────────────┬─────────────────+
                          │ Unix Domain Socket (Linux/Mac)
                          │ TCP localhost:8568 (Windows)
        +─────────────────▼─────────────────+
        │         ToolRecall Daemon         │  <- Gateway Layer
        │  ┌─────────────────────────────┐  │
        │  │   In-Memory LRU (Cache)     │  │
        │  └──────────────┬──────────────┘  │
        │  ┌──────────────▼──────────────┐  │
        │  │   SQLite WAL (Persistent)   │  │
        │  └─────────────────────────────┘  │
        │  ┌─────────────────────────────┐  │
        │  │   MCP Server Multiplexer    │  │
        │  └──────────────┬──────────────┘  │
        +─────────────────┼─────────────────+
                          │ Lazy-Loaded stdio Subprocesses
        +─────────────────▼─────────────────+
        │ [ Downstream MCP: GitHub / Time ] │  <- Execution Layer
        +───────────────────────────────────+

The daemon holds everything: the hybrid in-memory LRU + SQLite WAL cache, the MCP Multiplexer (manages subprocesses for external MCP servers), and the Security Gate (path allowlist, sensitive file blocklist, cognitive scan). All agents share one daemon via Unix Domain Sockets.

See Architecture for the full design.

---

MCP Multiplexer

Instead of each agent spawning separate subprocesses for every MCP server (GitHub, Postgres, time, fetch...), the daemon manages them:

• Lazy loading: servers boot on first call, not at daemon start (~0.01s vs ~1.7s per server)
• Idle timeout: inactive subprocesses killed after 15 min (configurable)
• Failure isolation: one server crash doesn't affect others (auto-reconnect, max 3 attempts)
• Secrets: API tokens loaded from ~/.toolrecall/.env, never exposed to the LLM

Agents connect to one MCP server in their config: toolrecall mcp.

See MCP Multiplexer for details.

---

Security

ToolRecall doesn't prevent prompt injection — it cages the consequences:

• Default-deny path allowlist: Without config, NO paths are readable. toolrecall init prompts for paths interactively.
• Sensitive file blocklist: .env, .ssh/, .pem, .aws/, etc. are blocked even inside allowed paths.
• allow_terminal=false (default): drops all cached_terminal calls into a void.
• os.path.realpath(): catches ../../../etc/shadow traversal before OS is touched.
• Cognitive Pre-Flight: Deterministic regex scan on MCP tool arguments for override instructions, jailbreak tags, exfiltration URLs. Zero LLM, ~0.001ms hot path.
• AST injection check: Parses tool arguments as Python AST — blocks exec(), eval(), __import__() calls.
• Daemon IPC via UDS: No open ports, immune to SSRF.

See Security Architecture for the full trust boundary.

---

Quick Reference — CLI (defaults marked, optional marked)

toolrecall init            Create default config.toml and .env  [required once]
toolrecall daemon          Start cache daemon                   [required]
toolrecall mcp             Start MCP Bridge                     [required — connects your agent]
toolrecall status          Cache status and stats               [optional]
toolrecall invalidate      Clear all caches                     [optional]
toolrecall reset-stats     Reset statistics counters            [optional]
toolrecall serve           Start HTTP proxy                     [optional — only for agents that can't speak MCP]
toolrecall nginx           Generate nginx config                [optional — only if you want HTTPS in front of the proxy]
toolrecall index           Build/update FTS5 knowledge database [optional]
toolrecall index-dir       Index a directory (e.g. Obsidian)    [optional]
toolrecall config-set      Set a config value                   [optional]

---

Agent Integration

ToolRecall registers its MCP tools under names like cached_read, cached_terminal, cached_write, cached_patch. How you connect depends on your agent:

Agent	How to connect	Cache mode
Hermes (hermes_init.py)	Transparent cache patches read_file → cached_read automatically	✅ Zero config
Claude Code	claude mcp add toolrecall -- toolrecall mcp then config snippet	⚡ Config snippet
Cursor	Add to .cursorrules	⚡ Config snippet
Cline	Add to .clinerules or cline_mcp_settings.json	⚡ Config snippet
Any MCP agent	Add server toolrecall: toolrecall mcp to your MCP config	⚡ Universal

Config snippets

Claude Code — add to ~/.claude/claude_dotfiles/claude.md:

ToolRecall is installed. When reading files, use `cached_read` via MCP instead of `read_file`.
When running terminal commands, use `cached_terminal` instead of `terminal`.

Cursor — add to .cursorrules at project root:

Use cached_read for file reads (MCP tool, faster on repeats).
Use cached_terminal for terminal commands (MCP tool, TTL-cached).

Cline — add to .clinerules or mention in the initial prompt:

When reading files, always use cached_read instead of read_file.
When running terminal commands, use cached_terminal.

Hermes Agent — transparent mode (monkey-patches native tools automatically):

# ~/.toolrecall/config.toml
[hermes]
transparent_cache = "transparent"   # default: "separate"

Then restart Hermes or type /reset. See Hermes Transparent Cache for details and risks.

---

Configuration

TOML (stdlib tomllib) or YAML (optional, requires pyyaml).

# ~/.toolrecall/config.toml (minimal config — toolrecall init creates a full one)
[mcp]
allowed_paths = ["/home/user/projects"]  # Add your project dirs — default-deny!
allow_terminal = false
default_ttl = 60

[mcp_multiplex]
enabled = true
servers = ["time", "fetch"]  # Enable MCP servers; GitHub needs GITHUB_TOKEN in .env

[proxy]
# ⚠️ REDACTED: IP addresses are placeholders. Proxy binds to 127.0.0.1 (localhost only).
# bind = "REDACTED_LOCALHOST_IP"
# port = 8567

[nginx]
# nginx is OPTIONAL — only needed if you want HTTPS/SSL in front of the proxy.
# Example: you host and code on the same machine; the proxy listens on localhost
# and nginx terminates SSL + forwards to it. Most users never need this.
# site_name = "toolrecall"
# domain = "example.com"
# ssl = false

TOOLRECALL_* environment variables override TOML.

---

Uninstall

pip uninstall toolrecall
python3 scripts/uninstall.py --force

Removes: daemon, systemd service, config, cache DB, logs.

---

Platform Support

Platform	Transport	Status
Linux	Unix Domain Sockets	✅ Tested in CI
macOS	Unix Domain Sockets	✅ Should work (POSIX). Not in CI.
Windows	TCP localhost:8568 fallback	⚠️ Core + transport tested. CLI works.

---

Documentation

• Architecture — daemon design, layers, IPC
• How It Works — quick technical overview
• MCP Multiplexer — single-daemon MCP management
• Benchmark — measured performance, token savings
• Knowledge DB — FTS5 indexing guide
• Docker Deployment — containerized stack
• Security Architecture — WAF details, trust boundary
• Troubleshooting — common fixes
• Appendix — comparison tables, OSI model, ROI, vision, audit
• Hermes Transparent Cache — auto-patching for Hermes Agent