dsh-tool-guardian

Tool Guardian

Your MCP servers, for ~300 tokens instead of ~28,000 — and tool results that stop flooding the window.

Why · Two ways to run it · Install · Measure it · Output ladder · DSH bundle · Changelog

An MCP server that sits in front of your other MCP servers and exposes three generic tools instead of dozens of specific ones — discovering the rest on demand — so tool definitions stop eating your context window before the model reads a word.

Companion to Context Guardian: Context Guardian compacts the conversation before the window fills; Tool Guardian keeps the tools from filling it in the first place. Two halves of the same problem.

	Without Tool Guardian	With it
7 MCP servers on a 32K model	28,689 tokens of schemas on every request (87.6 % of the window)	~300 tokens; a schema is fetched only when the model asks
A 50 KB shell result	51,165 characters land in the conversation	7,833 characters, the full original archived and one call away
DSH first request (measured)	46 tools, 37,154 characters of schema	22 tools, 18,503 characters
A backend that fails to start	an empty tool list the model silently works around	status UNKNOWN plus the real error (e.g. could not start: …), never an empty list

Why this exists

MCP tool definitions are re-sent on every single request, whether the model touches them or not. A handful of servers routinely comes to tens of thousands of tokens — often most of a small local model's window — before the first user message. On one real setup, seven MCP servers came to 28,689 tokens, 87.6% of a 32K window, as a fixed floor under everything else.

You have two ways to deal with that today, and both cost you something:

Approach	The cost
Load fewer MCP servers	You lose the capability entirely
Live with it	Two-thirds of the window is gone before you type

Tool Guardian is a third option that costs neither. It fronts all your servers and shows the model just three tools plus a one-line catalogue of server names (~300 tokens). The full schema for a tool is fetched only when the model asks for it:

list_capabilities(server?)      one line per tool — names and purpose
describe_tool(server, tool)     the full argument schema for ONE tool
call_tool(server, tool, args)   invoke it, return the result

Same idea as a search index: cheap catalogue always visible, detail on demand.

Model requirement (read this before you switch)

The whole design rests on one behaviour: the model must proactively call list_capabilities (then call_tool) when it needs a tool. Capable/frontier models do this reliably. Small local models are less certain — and, as the two measurements below show, the harness the model runs in matters as much as the model.

Measurement 1 — August 2026, OpenClaude, router mounted over MCP. Against a real studio stack, gpt-oss:20b and qwen3-30b-a3b both bypassed the router on ordinary tasks — even with the NEXT STEP nudge in every result and a dedicated router sub-agent priming them. They treated a tool name as a shell command or scripted their way around it. Part of that was the harness, not the models: gpt-oss:20b did call list_capabilities correctly but could not carry the discovery into call_tool inside a general task, and the one approach that completed the sequence (the sub-agent) was blocked by the harness's own agent-tool argument validation before it ever reached the router.

Measurement 2 — 2026-09-19 and 2026-09-21, DeepSeek Harness, router tools registered natively by the DSH bundle. The same model family (qwen3:30b-a3b-instruct-2507, 32K window) used the router with no bypass. 09-19, two sessions whose prompts named the tools: list_capabilities once, then list_capabilities ×8 → call_tool(luminari-scripts, service_status). 09-21, one session, three ordinary prompts that named no tool ("Which of the studio's services are running right now?", "How many n8n workflows do we have, and which were edited most recently?", "What did we learn last time a bridge commit wrote stale bytes?"): the call log shows list_capabilities(studio-jobs) → call_tool(studio-jobs, pipeline_status), then call_tool(n8n, list) (wrong name, ok: false) → call_tool(n8n, n8n_list_workflows) (ok: true, 12,963 chars shaped by the ladder) — the model corrected itself from the router's error — and zero bypass rows; the third prompt was answered with the companion plugin's recall/search plus built-in glob/read, which is the right tool, not a bypass. That is three sessions and one model: enough to show Measurement 1 is not a verdict on these models, not enough to promise yours will behave.

So: --selftest proves the saving and that your backends start — it does not prove your model will drive the router in your harness. Test discovery→call with your actual model and harness before committing, and measure rather than guess: every router call is appended to ~/.tool-guardian/calls.jsonl (TOOL_GUARDIAN_CALL_LOG), and under DSH a shell call that does a router tool's job is logged there as kind: "bypass" (and can be nudged or denied). A session's worth of that file tells you whether your model asks. If it won't, expose a small curated, visible subset of servers instead of routing everything behind a catalogue the model never opens.

Two ways to run it

It is one repo and one Python router. Pick the front door that matches your harness — both stay supported.

	MCP server (any MCP client)	Native DSH bundle
Works with	Claude Code, Claude Desktop, OpenClaude, Cursor, anything that speaks MCP over stdio	DeepSeek Harness
Install	pip install tool-guardian	dsh plugin --profile add dsh-tool-guardian
Hides MCP schemas behind 3 router tools	yes	yes, registered natively
Output ladder on results	results of call_tool	every tool's result (bash, grep, web_fetch, ...)
Tool groups with token prices	list_groups_with_costs	plus activate_group, and DSH's own built-in tools can be grouped and hidden too
Notices a shell call doing a router tool's job	—	logs, nudges or denies it
Configured by	tool-guardian.json + TOOL_GUARDIAN_* env	the tool-guardian patch row or DSH settings; the same env vars win

flowchart LR
    A["Your agent
(Claude Code, DSH, any MCP client)"] -->|"3 tools, ~300 tokens"| B["Tool Guardian"]
    B -->|"on demand"| C["filesystem"]
    B -->|"on demand"| D["git"]
    B -->|"on demand"| E["n8n, database, ..."]
    B -. "big result" .-> F[("archive
retrieve_spill")]
    B -->|"shaped result"| A

Where it sits

your CLI / agent (Claude Code, OpenClaude, any MCP client)
    -> Tool Guardian          (this project — one MCP server)
        -> your real MCP servers (filesystem, git, n8n, database, ...)

You point your client at one MCP server — Tool Guardian — and give Tool Guardian the same mcpServers config you'd have given the client. It starts your servers, keeps them warm, and proxies calls through on demand.

Install

pip install tool-guardian

Pure standard library — nothing else to install.

Configure

Quickest way: you probably already have your servers set up in another MCP client.

tool-guardian-setup import    # copies them from Claude Desktop / Cursor / Windsurf / .mcp.json, asks before writing
tool-guardian-setup doctor    # checks the config, each server's command, your .env and ${VARS}, and says how to fix each problem

Adding one more server is one command -- everything after -- is the server's command line, exactly as you would type it:

tool-guardian-setup add git -- uvx mcp-server-git
tool-guardian-setup add github --env GITHUB_TOKEN='${GITHUB_TOKEN}' --description "issues and PRs" -- npx -y @modelcontextprotocol/server-github
tool-guardian-setup list      # name, command, and the group each server's tools land in
tool-guardian-setup remove git

add writes to the same config doctor finds (under DSH that is $TOOL_GUARDIAN_CONFIG), backs it up first, refuses a name that already exists unless you pass --replace, checks the command is on your PATH, warns about any ${VAR} that is not set in your environment or .env, and ends with the doctor result for that one server. Then restart your MCP client (or start a new DSH session) to load it. Every server you add is automatically behind the router's 3 tools: with no groups config it gets its own group named after the server; with a custom groups config it lands in other until you add it to a group.

import writes ~/.tool-guardian/mcp.json (backing up any existing file), skips what it can't route yet (HTTP/SSE servers) and prints the one entry to put in your client instead. Inside DSH (npm install) run the same thing as python /tg_setup.py doctor.

Or write the config by hand:

Tool Guardian reads the standard mcpServers block (the same shape Claude Desktop / Claude Code and most MCP clients use):

{
  "mcpServers": {
    "files": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/data"]
    },
    "git": {
      "command": "uvx",
      "args": ["mcp-server-git"],
      "description": "git status / diff / commit / log"
    }
  }
}

An optional per-server "description" enriches the catalogue the model sees. Without one, the hint is derived from that server's own tool names at startup.

Config is searched in order: --config PATH, $TOOL_GUARDIAN_CONFIG, ./mcp.json, ./.mcp.json, ~/.tool-guardian/mcp.json.

Environment and .env

Tool Guardian loads a .env itself and expands variables in your backend args, so secrets don't have to be exported into the environment by whatever launches it.

• .env autoload. On startup it looks for a .env in this order: an explicit path, $TOOL_GUARDIAN_ENV, then an upward search — starting at the config file's directory (or the cwd) and walking up to 5 parent directories, loading the first .env it finds. This lets your config live in a nested folder while the .env sits at the project root. Values already in the real environment win over the file; a missing .env is a no-op, never an error.

  Example — config nested under the project, .env at the root:

  myproject/
  ├── .env                  not found
  2. myproject/config/.env        -> not found
  3. myproject/.env               -> FOUND  (stops here)
  

• Variable expansion. ${VAR}, $VAR and %VAR% are expanded in each backend's args from the environment; unknown variables are left as-is. Keep a secret in .env and reference it in a backend arg:

  "args": ["-y", "mcp-remote", "https://app.openseo.so/mcp",
           "--header", "Authorization: Bearer ${OPENSEO_API_KEY}"]
  

Run

Point your MCP client at Tool Guardian as a single stdio server:

{
  "mcpServers": {
    "tool-guardian": {
      "command": "tool-guardian",
      "args": ["--config", "/path/to/your/mcp.json"]
    }
  }
}

Everything your servers can do is still reachable — the model just discovers it in two steps (list_capabilities → call_tool) instead of paying for all of it up front.

See what it saves

tool-guardian --selftest

Starts your configured servers, prints the catalogue, and reports the tokens the three router tools cost versus loading every server's tools directly — e.g. "router tools cost ~310 tokens vs ~28,700 for the full set behind them → ~28,390 freed on every request."

It keeps tool results small too (0.3.0)

Definitions are half the problem; one 40 KB build log is the other half. Every call_tool result now goes down a deterministic output ladder before the model sees it:

result	what the model gets
under ~1.2k chars, or from a read-class tool	untouched, byte for byte
an error over 300 chars	head + tail summary
JSON array / CSV ≥ 10k	keys, first and last items, counts
shell-style output ≥ 8k	head, evenly spaced samples (with line numbers), tail — [exit code: …] always kept
a unified diff	every change, plus the context right next to it
anything else ≥ 1.2k	cleaned losslessly: ANSI stripped, blank runs collapsed, repeated lines counted

Nothing is lost silently. Before any lossy step the full original is archived, the result says so in one line, and the model can call retrieve_spill(id, grep=…) to read it back. If the archive cannot be written, the original is returned instead. Same input, same output, always — so provider prompt caches keep hitting. TOOL_GUARDIAN_LADDER=0 turns it off.

list_groups_with_costs prices each tool group in context tokens, and every router call is logged (argument values never are) to ~/.tool-guardian/calls.jsonl so you can measure whether your model actually uses the router.

Native DeepSeek Harness (DSH) bundle

The same repo is an installable DSH bundle, dsh-tool-guardian. The Python router is unchanged — the bundle is a bridge to it, not a rewrite, and the MCP server above keeps working.

dsh plugin --profile  add dsh-tool-guardian        # or a path to a checkout (run `pnpm install` in it first)
dsh --profile  --dump-config                        # shows a "# == dsh-tool-guardian" layer

Inside DSH it (1) registers the router tools natively, so your MCP backends' schemas never enter a request unless you activate their group (activeGroups, or the activate_group tool, which quotes the token cost first); (2) runs the output ladder on every tool's result — bash, grep, web_fetch, all of them — through tools/post-execute, so do not mount dsh-trim beside it; (3) notices shell calls that do a router tool's job and logs, nudges (default) or denies them (bypass.mode). Configure it in the profile's cordis.patch.yml by overriding the tool-guardian row, or through the DSH settings namespace tool-guardian; the existing TOOL_GUARDIAN_* environment variables win over both. Python is found at $TOOL_GUARDIAN_PYTHON, then a .venv beside the package, then python/python3 on PATH (3.9+, standard library only).

The result-shaping design follows dsh-trim (shuistama, MIT): next() first, fail open, archive before anything lossy.

Design notes (the parts that matter)

• Failure is loud, on purpose. A router is a single point of failure: without one a broken server costs you that server; behind one it c