clear-your-tools 0.0.1

Clear Your Tools (CYT) — dynamic tool gating for eliminating the MCP/tools tax

Clear Your Tools

Clear Your Tools is a reverse proxy for coding agents such as Claude Code. It sits between the agent and upstream LLM providers (Anthropic-compatible APIs on OpenRouter, Novita, DeepInfra, and others), intercepts each request, and shrinks the tool payload before forwarding it upstream. Can be easily adopted for other harness agents.

Large MCP catalogs can add tens of thousands of tokens of tool-schema overhead on every turn. Clear Your Tools removes irrelevant tools and trims irrelevant optional parameters while always keeping required fields for tools that stay in the request.

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How it works

Agent (Claude Code, etc.)
        │
        ▼
Clear Your Tools proxy  ──► extract user query from messages
        │                   decompose each tool schema
        │                   score / filter with reranker (or LLM pruning)
        │                   recompose pruned tool list
        ▼
Upstream provider (OpenRouter, Anthropic, Novita, …)

On each intercepted request the proxy:

1. Extracts the user query from the conversation (latest user turn, with message cleanup).
2. Decomposes tool schemas into a catalog of chunks: each tool root keeps required properties; optional properties are split into separate searchable units.
3. Runs the pruning pipeline configured in config.yaml (default: rerank; or llm).
4. Recomposes surviving tools — required properties always remain; only optional properties that look relevant to the query are merged back in.
5. Forwards the modified request to the upstream provider with the smaller tools array.

Pruning pipeline

Stage	Model (default)	When it runs	What it does
rerank	Qwen3-Reranker-8B (DeepInfra)	≥ models.rerankers.minimum_tools tools (default 29)	Scores every catalog chunk against the user query; drops low-scoring tools and optional props.
llm	Mercury 2 or GPT-OSS-120B (OpenRouter)	≥ models.llm.minimum_tools tools (default 50), after rerank	LLM selects which catalog chunks to keep; can remove entire tools more aggressively.

Recommendations:

• Fewer than ~30 tools — pruning is skipped automatically; the overhead is usually not worth it.
• 30–50 tools — enable the rerank pipeline (default). This is the sweet spot for the reranker pruner.
• 50+ tools — keep rerank or use llm. rerank can be pipelined into LLM as a second stage (pipeline: [rerank, llm]) for stronger tool-level filtering on large catalogs.

Configure thresholds in config.yaml (or ~/.configs/cyt/config.yaml):

models:
  rerankers:
    minimum_tools: 29
  llm:
    minimum_tools: 50

pruning:
  pipeline:
    - rerank
    # - llm

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Quick start

Requires Python 3.13+ (see pyproject.toml).

Install

From PyPI (proxy + pruners):

uv pip install 'clear-your-tools[all]'
# or
uv tool install 'clear-your-tools[all]'

For local development, dependencies are managed with uv:

uv sync --all-extras

Copy API keys (or use ~/.configs/cyt/.env):

cp src/.env.example .env
# Edit .env — at minimum DEEPINFRA_API_KEY (reranker) and OPENROUTER_API_KEY (upstream + optional LLM stage)

Run the proxy

Installed CLI:

cyt-rproxy serve --port 8834

From a dev checkout:

uv run cyt-rproxy serve --port 8834

Default listen port: 8834 (from bundled defaults.yaml or ~/.configs/cyt/config.yaml).

Point Claude Code at the proxy:

export ANTHROPIC_BASE_URL="http://localhost:8834/anthropic"
export OPENROUTER_API_KEY="..."
export ANTHROPIC_AUTH_TOKEN="${OPENROUTER_API_KEY}"
claude --model haiku 'say hi' -p

The default upstream in config.yaml is OpenRouter's Anthropic-compatible endpoint. Change network.proxy.reverse.upstreams to target a different provider URL.

Debug without calling upstream

cyt-rproxy serve --debug-dry-run --port 8834

Writes transformed request snapshots to {endpoint}.log (e.g. anthropic.log).

View pruning stats savings

cyt-rproxy stats totals
cyt-rproxy stats summary --period day
cyt-rproxy stats events --limit 20

Stats are stored in ~/.configs/cyt/stats.db by default.

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HTTP/2 and TLS

Some clients prefer HTTP/2. Generate a local certificate (gitignored under src/crt/):

mkdir -p src/crt
openssl req -x509 -nodes -days 365 -newkey rsa:4096 \
  -keyout src/crt/key.pem \
  -out src/crt/cert.pem \
  -subj "/CN=localhost" \
  -addext "subjectAltName=DNS:localhost,IP:127.0.0.1"

Trust the cert on macOS: Keychain Access → System → import cert.pem → Trust → "Always Trust".

Run with HTTP/2:

uv pip install h2 'hypercorn[h2]'
cyt-rproxy serve --http2-serve \
  --ssl-keyfile src/crt/key.pem \
  --ssl-certfile src/crt/cert.pem \
  --port 8834

TLS settings can also live in config.yaml under network.proxy.reverse.http2.ssl.

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Pruning policies

Two tool categories with different defaults:

Category	Default policy	Examples	Typical prefix
System tools	prune_optional	Read, Write, Agent	(no mcp__ prefix)
MCP tools	prune_all	Tools from MCP servers	mcp__…

Set defaults in config.yaml:

defaults:
  system_tool_policy: prune_optional
  mcp_tool_policy: prune_all

Policy options

Policy	Behavior
always_include	No pruning — full tool schema every turn.
prune_optional	Tool always included; irrelevant optional properties dropped. Required properties always kept.
prune_all	Entire tool may be removed if irrelevant. If kept, required properties stay; optional ones trimmed.

prune_all on MCP tools saves the most tokens. With ~100 tools, expect up to ~95% reduction in tool-schema tokens.

Per-tool overrides

pruning:
  per_tool:
    Agent: prune_optional
    mcp__hedl__hedl_convert_from: prune_optional
    mcp__hedl__batch: prune_all
    mcp__fff__multi_grep: always_include

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FAQ

Doesn't pruning burn more tokens than it saves?

The reranker and weak LLM used for pruning are much cheaper per token than the main model (e.g. Claude Sonnet). You may spend extra tokens on pruning, but they cost a fraction of what you save on the main request. Add input_cost_per_token and output_cost_per_token to config.yaml to track savings.

Example pricing (input tokens):

Model	Cost per 1M input tokens
Claude Sonnet 4.6	$3.00
Qwen-Reranker-8B	$0.050
GPT-OSS-120B	$0.14
Inception Mercury 2	$0.25

The weak models such as Mercury 2 or GPT-OSS-120B returns only the IDs of tools to keep, so its output stays extremely small. Rerankers do not count output tokens and are usually much cheaper than a strong LLM.

Rule of thumb: saving 1M Sonnet input tokens is still worthwhile even if pruning uses up to ~10M Mercury tokens — roughly a 1:10 cost ratio. The reranker has roughly a 1:60 cost ratio.

In practice, pruning usually adds modest overhead. Worst case (no tools pruned), you might pay ~$3.30 instead of $3.00. With typical pruning (40–95% of tool tokens removed), tool-schema cost drops from ~$3.00 to roughly $0.15–$1.80, plus ~$0.30 for pruning — about $0.45–$2.10 total for tool-related cost, or roughly 30–85% savings depending on policy.

Why don't I see 30–85% savings on my total request?

Those numbers apply to tool schemas only, not the full prompt (system message, conversation history, user message, etc.). Clear Your Tools prunes tools based on the user request; the rest of the request is unchanged.

How much you save overall depends on:

• How many tools you have — more MCP servers mean a larger share of the request is tool schemas. We do not recommend using CYT below 50 tools.
• Which pruning policy you use — see Pruning policies.

Estimate total savings on a captured request:

uv run count_request_tokens.py \
  --tool-savings-percent 85 \
  --requestfile temp_example_claude_call.json

temp_example_claude_call can be obtained from the proxy running in debug mode.

With ~100 tools and prune_all, expect ~85–95% savings on tool tokens and typically ~30%+ savings on the full request. The more tools you have the more overall savings you'll see.

Where can I see how many tools and parameters an MCP server has?

The popular Fetch MCP server is a good example. On its Tools tab: 4 tools, each with 4 parameters (1 required, 3 optional) — 16 parameters total.

If the user asks to "fetch the Markdown of a webpage", the prune_all typically keeps only the Fetch Markdown tool with its required parameter plus any optional parameters that look relevant. Unrelated tools (e.g. Read file) are dropped entirely.

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Repository layout

.
├── README.md
├── pyproject.toml
├── count_request_tokens.py      # estimate savings on a captured request JSON
└── src/
    └── cyt/                       # installable package (Clear Your Tools)
        ├── config/                # load_config, defaults.yaml
        ├── common/                # catalog_paths, token_usage, pricing
        ├── indexer/               # build, retrieve, catalog_io
        ├── pruners/               # llm, rerank, policies
        └── proxy/                 # transport, reverse, anthropic, stats, cli

Library usage

from cyt.indexer import CatalogIndex, build_catalog_index, load_catalog, retrieve_tools
from cyt.pruners import rerank_catalog_dict, llm_catalog_dict
from cyt.pruners.policies import configure_policies_from_config
from cyt.proxy.reverse import create_app  # requires clear-your-tools[proxy]

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Configuration reference

Main config file: config.yaml in the working directory, or ~/.configs/cyt/config.yaml (created on first run). Bundled defaults ship in the package as cyt.config.defaults.yaml.

Section	Purpose
defaults.system_tool_policy / mcp_tool_policy	Default pruning behavior for system vs MCP tools
defaults.remote.reranking_model_nick / llm_model_nick	Model nicknames for pruning stages
pruning.pipeline	Ordered list of stages: rerank, llm
pruning.per_tool	Per-tool policy overrides
models.rerankers / models.llm	Remote model definitions, API keys, minimum tool counts
network.proxy.reverse	Listen port, upstream URLs, HTTP/2, TLS
stats	Stats DB path, optional full tool JSON storage

Environment variables (see src/.env.example):

• DEEPINFRA_API_KEY — reranker stage
• OPENROUTER_API_KEY — upstream forwarding and optional LLM stage

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Limitations

This implementation requires running as reverse proxy with supported agents such as Claude Code, Codex, OpenCode etc.

Cursor for instance can't run with reverse proxy and only supports forward proxy, though the requests sent via forward proxy are still encrypted and not visible for manipulation and pruning.

This functionality logically is more suitable to be accompanied with an MCP Aggregator that takes all the tools from actual MCP servers on backend and serves only the relevant tools to the agent. Though in theory sound concept, in practice MCP protocol Specification has limitations not allowing this to happen:

• MCP is not designed to be integrated with Agent hooks
• MCP Client and servers are initialized before agent starts its session leading to MCP is not aware of agent sessions or sub-agents and can't reliably target which agent session or subagent see which tools, so pruning would become unreliable.

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License

See LICENSE.