Dehydrator

Client-side tool search for LLM APIs. Use thousands of tools without bloating the context window.

Works with Anthropic, OpenAI, and any OpenAI-compatible provider (Groq, OpenRouter, Chutes, etc.). Accepts tools from MCP servers natively. Search runs on BM25 (local, free), on Jev by TypeSafe AI (a decision model that reads meaning, not keywords), or on both.

The problem

LLM APIs require you to send all tool definitions in every request. With 100+ tools, this wastes tokens and degrades tool selection. Anthropic offers a server-side tool_search_tool_bm25, but it's not available on all platforms (e.g. Bedrock) and doesn't work with ZDR. Dehydrator gives you the same capability client-side, so it works everywhere — with any provider.

How it works

Dehydrator wraps your LLM client and replaces the full tool list with a single tool_search tool. When the model needs a tool, it searches by description. Dehydrator intercepts the call, runs the search locally, and re-calls the API with only the matched tools injected.

User request
    │
    ▼
┌─────────────────────────────┐
│  API call #1                │
│  tools = [tool_search]      │
│                             │
│  Model responds:            │
│  tool_search("send email")  │
└─────────────┬───────────────┘
              │  intercepted by Dehydrator
              ▼
┌─────────────────────────────┐
│  search (BM25 and/or Jev)   │
│  → matches: send_email,     │
│     send_slack_message       │
└─────────────┬───────────────┘
              │
              ▼
┌─────────────────────────────┐
│  API call #2                │
│  tools = [tool_search,      │
│           send_email,        │
│           send_slack_message]│
│                             │
│  Model responds:            │
│  send_email({...})          │
└─────────────────────────────┘
              │
              ▼
        Returned to you

Only the tools the model actually needs are ever sent. Discovered tools persist across turns within a conversation.

Installation

pip install dehydrator

No extra dependency is needed for Jev; it uses the standard library.

Quick start

Anthropic

import anthropic
from dehydrator import DehydratedClient

client = DehydratedClient(
    anthropic.Anthropic(),
    tools=tools,
    top_k=5,
)

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
)

The response is a standard anthropic.types.Message.

OpenAI-compatible (OpenAI, Groq, OpenRouter, Chutes, etc.)

from openai import OpenAI
from dehydrator import OpenAIDehydratedClient

client = OpenAIDehydratedClient(
    OpenAI(),
    tools=tools,
    top_k=5,
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
)

Works with any client that implements client.chat.completions.create(). No openai import required — fully duck-typed.

MCP tools

Tools from MCP servers use inputSchema (camelCase) or input_schema. Dehydrator accepts both, as dicts or as mcp.types.Tool objects:

tools = (await session.list_tools()).tools   # list[mcp.types.Tool]

from dehydrator import ToolIndex
index = ToolIndex.from_mcp(tools, top_k=5)

Choosing a search mode

Mode	How to enable	Cost	Best for
BM25 (default)	nothing	free, offline	Tool descriptions and user queries share vocabulary.
BM25 + Jev	`reranker=JevReranker()`	~$0.00002 / search	Best overall: BM25 shortlists 10, Jev picks. Keeps BM25 recall, fixes its ranking mistakes.
Jev only	`search="jev"`	~$0.00016 / search at 139 tools	Queries that share no words with tool descriptions: paraphrases, other languages.

# BM25 + Jev
client = DehydratedClient(anthropic.Anthropic(), tools=tools, reranker=JevReranker())

# Jev only
client = DehydratedClient(anthropic.Anthropic(), tools=tools, search="jev")

Both work identically on OpenAIDehydratedClient and the async clients.

Why not always Jev only? BM25 is a hard gate: when the query shares no tokens with any description, BM25 returns nothing and Jev is never asked. "Покажи останні 3 коміти" returns no tools in BM25 or hybrid mode and routes to git_log at probability 1.00 in Jev-only mode. On the other hand Jev concentrates probability on the winner and leaves the tail unordered, so if you inject several tools per search (top_k > 1) the hybrid's recall is higher. Pick by your queries; numbers are in Benchmarks.

Jev

Jev by TypeSafe AI is a decision model, not an LLM. It does not generate text. Given a state (here: the user's query) and a typed question (here: "which of these tools should be called?", with every tool as an option), it returns a calibrated probability for each option plus a confidence score, in about 400 ms. Dehydrator calls it directly at TypeSafe's API as jev-latest, or through Vercel AI Gateway as typesafe-ai/jev. Input costs $0.042 per million tokens; output is free. Model docs: docs.typesafe.ai.

Setup

Jev is reachable two ways. Dehydrator speaks to both with the same code and picks the provider from the environment.

Provider	Key	Endpoint	Model
TypeSafe AI (default)	`TYPESAFE_API_KEY` from console.typesafe.ai	`api.typesafe.ai/v1/systemone`	`jev-latest`
Vercel AI Gateway	`AI_GATEWAY_API_KEY` from vercel.com/ai-gateway	`ai-gateway.vercel.sh/v1/evaluate`	`typesafe-ai/jev`

export TYPESAFE_API_KEY=apikey_... (or AI_GATEWAY_API_KEY=vck_...).
Add reranker=JevReranker() or search="jev" to your client.

If both keys are set, TypeSafe is used. Force one with JevReranker(provider="gateway"). On the benchmark both return identical rankings; TypeSafe's API accepted 8 concurrent requests without throttling where the gateway returned 429 above about 3.

`JevReranker`

Re-orders a list of candidate tools for a query. Used by the hybrid mode and by JevIndex; also usable on its own.

from dehydrator import JevReranker

rr = JevReranker(
    api_key=None,          # default: TYPESAFE_API_KEY, else AI_GATEWAY_API_KEY
    provider=None,         # "typesafe" | "gateway"; auto-detected from env
    model=None,            # provider default: jev-latest / typesafe-ai/jev
    min_probability=0.0,   # drop candidates Jev scores below this
    timeout=30.0,
    retries=3,             # on 429/529/5xx, exponential backoff
)
ranked = rr.rerank("send an email", tools)   # list[str], best first

rr.last_probabilities   # {"send_email": 0.93, "send_slack_message": 0.07}
rr.last_confidence      # 0.86  (1 = concentrated, 0 = spread out)
rr.last_error           # None, or the exception if the last call fell back

If a request fails for any reason, rerank returns the candidates in the order it received them and sets last_error. In hybrid mode that means BM25 order, so enabling Jev can never make results worse than plain BM25.

min_probability shrinks the injected tool list: with min_probability=0.05, tools Jev considers irrelevant are dropped even if top_k has room.

`JevIndex`

Jev-only search. Same interface as ToolIndex.

from dehydrator import JevIndex

index = JevIndex(tools, top_k=5, batch_size=200, finalists=10)
index.search("is my working copy dirty")     # ["git_status", ...]
index.last_probabilities, index.last_confidence

Jev accepts at most 255 options per question. Above batch_size tools, JevIndex runs a tournament: each batch gets a Jev round and the top finalists from every batch meet in a final round. With 1,000 tools that is six requests per search. Under 200 tools it is one request.

Reading the output

Use the probabilities and confidence, not just the winner:

git_status 1.00, confidence 1.0: route and move on.
move_file 0.88, create_directory 0.12, confidence 0.86: two steps may be needed.
search_files 0.92, read_multiple_files 0.07: the query was vague ("look at the tests"); consider asking the user.

A useful pattern is to escalate to a human or a larger model when last_confidence is below a threshold you choose per action.

Dehydrator as an MCP gateway

dehydrator-mcp (installed with the package) runs Dehydrator as an MCP server in front of any number of other MCP servers. Your coding agent sees two tools, tool_search and call_tool, instead of hundreds, and every search is ranked by Jev.

Configuration is by environment variables:

Variable	Meaning
`TYPESAFE_API_KEY`	TypeSafe AI key (preferred).
`AI_GATEWAY_API_KEY`	Vercel AI Gateway key, used when no TypeSafe key is set. Without either, search is plain BM25.
`DEHYDRATOR_SERVERS`	JSON map of upstream servers: `{"name": {"command": "...", "args": [...], "env": {...}}}`. Default: filesystem + git on the current directory.
`DEHYDRATOR_SEARCH`	`jev` (Jev only) or `bm25` (default; BM25 shortlist re-ranked by Jev when a key is set).
`DEHYDRATOR_TOP_K`	Tools returned per search. Default 5.

Example upstream set, used in the snippets below:

export DEHYDRATOR_SERVERS='{
  "fs":     {"command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/my/project"]},
  "git":    {"command": "uvx", "args": ["mcp-server-git"]},
  "github": {"command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"],
             "env": {"GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_..."}}
}'

Claude Code

claude mcp add dehydrator \
  -e TYPESAFE_API_KEY=$TYPESAFE_API_KEY \
  -e DEHYDRATOR_SERVERS="$DEHYDRATOR_SERVERS" \
  -e DEHYDRATOR_SEARCH=jev \
  -- uvx --from dehydrator dehydrator-mcp

Then /mcp shows dehydrator with two tools. Add -s user to make it available in every project.

OpenAI Codex CLI

~/.codex/config.toml:

[mcp_servers.dehydrator]
command = "uvx"
args = ["--from", "dehydrator", "dehydrator-mcp"]

[mcp_servers.dehydrator.env]
TYPESAFE_API_KEY = "apikey_..."
DEHYDRATOR_SEARCH = "jev"
DEHYDRATOR_SERVERS = '{"fs": {"command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/my/project"]}}'

Cursor

.cursor/mcp.json in the project (or ~/.cursor/mcp.json globally):

{
  "mcpServers": {
    "dehydrator": {
      "command": "uvx",
      "args": ["--from", "dehydrator", "dehydrator-mcp"],
      "env": {
        "TYPESAFE_API_KEY": "apikey_...",
        "DEHYDRATOR_SEARCH": "jev",
        "DEHYDRATOR_SERVERS": "{\"fs\": {\"command\": \"npx\", \"args\": [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/my/project\"]}}"
      }
    }
  }
}

Claude Desktop

Same JSON shape as Cursor, in claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\).

Any other MCP client

Anything that can launch a stdio MCP server works: run dehydrator-mcp with the environment above. pipx run --spec dehydrator dehydrator-mcp is an alternative to uvx. tool_search results include each tool's argument schema and Jev probability; call_tool forwards to whichever upstream server owns the tool.

API

`DehydratedClient(client, tools, *, top_k=5, always_available=None, max_search_rounds=3, reranker=None, search="bm25")`

Wraps an anthropic.Anthropic client.

Parameter	Type	Description
`client`	`anthropic.Anthropic`	An Anthropic SDK client instance
`tools`	`list[dict]`	Tool definitions (Anthropic or MCP format)
`top_k`	`int`	Max tools returned per search (default: 5)
`always_available`	`list[str]`	Tool names to include in every request, bypassing search
`max_search_rounds`	`int`	Max search iterations per `create()` call (default: 3)
`reranker`	`Reranker \| None`	Re-orders the BM25 shortlist, e.g. `JevReranker()`
`search`	`"bm25" \| "jev"`	Search backend. `"jev"` uses `JevIndex` and no BM25

Methods

client.messages.create(**kwargs) — Same signature as the Anthropic SDK. The tools kwarg is ignored (Dehydrator manages tools). Returns anthropic.types.Message.
client.reset_discoveries() — Clears discovered tools. Call this when starting a new conversation.
client.inner — Access the underlying anthropic.Anthropic client.

`AsyncDehydratedClient`

Same API as DehydratedClient, but wraps anthropic.AsyncAnthropic and create() is async.

`OpenAIDehydratedClient(client, tools, *, top_k=5, always_available=None, max_search_rounds=3, reranker=None, search="bm25")`

Wraps any OpenAI-compatible client. Same parameters as DehydratedClient; client is any object with client.chat.completions.create(), and tools are converted to OpenAI function format automatically.

Methods

client.chat.completions.create(**kwargs) — Same signature as the OpenAI SDK. The tools kwarg is ignored. Returns the provider's response object.
client.reset_discoveries() — Clears discovered tools.
client.inner — Access the underlying client.

`AsyncOpenAIDehydratedClient`

Same API as OpenAIDehydratedClient, but create() is async.

`ToolIndex(tools, *, top_k=5, reranker=None, candidates=10)`

The BM25 index, standalone. With a reranker, BM25 retrieves candidates tools, the reranker orders them, and top_k are returned.

from dehydrator import ToolIndex

index = ToolIndex(tools, top_k=5)
matched_names = index.search("weather forecast")
matched_tools = index.get_tools(matched_names)

index = ToolIndex.from_mcp(mcp_tools, top_k=5, reranker=JevReranker())

`JevIndex`, `JevReranker`, `Reranker`, `SearchIndex`

See Jev. Reranker and SearchIndex are protocols: implement rerank(query, tools) -> list[str] to plug in your own reranker, or search / get_tools / get_tool / tool_names to supply your own index to the adapters.

Always-available tools

Some tools should always be in context (e.g. a help tool). Pass their names to always_available:

client = DehydratedClient(
    anthropic.Anthropic(),
    tools=tools,
    always_available=["help", "get_current_user"],
)

These tools are sent in every request without requiring a search.

Multi-turn conversations

Discovered tools persist across calls to create(). If the model found send_email in turn 1, it's still available in turn 2 without re-searching.

Call client.reset_discoveries() when starting a new conversation:

# Turn 1: model discovers send_email
response = client.messages.create(...)

# Turn 2: send_email is still available
response = client.messages.create(...)

# New conversation
client.reset_discoveries()

Benchmarks

Full results, failure cases and calibration check: BENCHMARKS.md.

Benchmarked against 139 real tool definitions from 6 popular MCP servers (Chrome DevTools, GitHub, Playwright, Filesystem, Git, Notion) and 30 ground-truth queries.

Token savings

Sending all tools in every request is expensive. Dehydrator replaces them with a single tool_search tool and only injects the tools the model actually needs:

Tools	top_k=3	top_k=5	top_k=10	Baseline
50	274 tokens (94%)	349 tokens (93%)	678 tokens (86%)	4,864
100	274 tokens (97%)	349 tokens (96%)	678 tokens (92%)	8,954
200	274 tokens (98%)	349 tokens (98%)	678 tokens (96%)	18,159

With 200 tools and top_k=5, you go from 18,159 → 349 tokens per request — a 98% reduction.

Search quality

Metric	BM25	BM25 + Jev	Jev only
Precision@1	93.3%	100.0%	100.0%
Recall@1	59.7%	66.4%	66.4%
Recall@3	88.6%	91.9%	91.9%
Recall@5	95.3%	96.1%	91.9%
Recall@10	98.3%	98.3%	91.9%
MRR	95.8%	100.0%	100.0%
input tokens / search	0	553	3,741
cost / search	$0	$0.00002	$0.00016
median latency	<1 ms	386 ms	412 ms

All three modes find a correct tool in the top 10 for 30/30 queries. BM25 alone misses top-1 on two lexical traps (get_workflow_run over run_workflow, create_pull_request_review over create_pull_request); both Jev modes fix them. Jev-only trades recall in the tail for independence from vocabulary.

Jev-only above 200 tools uses the tournament, several sequential requests per search. See BENCHMARKS.md for timings on both providers.

Run the benchmarks

uv run python benchmarks/search_quality.py       # BM25, local, no API key
uv run python benchmarks/token_savings_openai.py  # local, uses tiktoken
uv run python benchmarks/search_quality_jev.py    # all three modes, needs TYPESAFE_API_KEY or AI_GATEWAY_API_KEY

Examples

File	What it shows
`examples/mcp_chat.py`	Interactive chat: real MCP servers, LLM via Vercel AI Gateway, Jev search
`examples/e2e_gateway.py`	Five prompts with and without Jev against the benchmark corpus

The MCP examples use TYPESAFE_API_KEY or AI_GATEWAY_API_KEY for Jev, and AI_GATEWAY_API_KEY for the LLM they drive. Run with uv run --with openai python examples/<file>. The MCP gateway is not an example but part of the package: see Dehydrator as an MCP gateway.

Limitations

No streaming — stream=True raises NotImplementedError. Planned for a future release.
Reserved tool name — You cannot have a tool named tool_search. Dehydrator will raise ValueError if you do.
Jev option cap — one Jev question holds at most 255 tools; JevIndex handles more via the tournament.

Development

git clone https://github.com/Arrmlet/dehydrator.git
cd dehydrator
uv sync

uv run pytest           # tests
uv run ruff check src/  # lint
uv run mypy src/        # type check

License

MIT

Name		Name	Last commit message	Last commit date
benchmarks		benchmarks
examples		examples
src/dehydrator		src/dehydrator
tests		tests
.gitignore		.gitignore
BENCHMARKS.md		BENCHMARKS.md
CHANGELOG.md		CHANGELOG.md
README.md		README.md
pyproject.toml		pyproject.toml

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Dehydrator

The problem

How it works

Installation

Quick start

Anthropic

OpenAI-compatible (OpenAI, Groq, OpenRouter, Chutes, etc.)

MCP tools

Choosing a search mode

Jev

Setup

JevReranker

JevIndex

Reading the output

Dehydrator as an MCP gateway

Claude Code

OpenAI Codex CLI

Cursor

Claude Desktop

Any other MCP client

API

DehydratedClient(client, tools, *, top_k=5, always_available=None, max_search_rounds=3, reranker=None, search="bm25")

Methods

AsyncDehydratedClient

OpenAIDehydratedClient(client, tools, *, top_k=5, always_available=None, max_search_rounds=3, reranker=None, search="bm25")

Methods

AsyncOpenAIDehydratedClient

ToolIndex(tools, *, top_k=5, reranker=None, candidates=10)

JevIndex, JevReranker, Reranker, SearchIndex

Always-available tools

Multi-turn conversations

Benchmarks

Token savings

Search quality

Run the benchmarks

Examples

Limitations

Development

License

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

`JevReranker`

`JevIndex`

`DehydratedClient(client, tools, *, top_k=5, always_available=None, max_search_rounds=3, reranker=None, search="bm25")`

`AsyncDehydratedClient`

`OpenAIDehydratedClient(client, tools, *, top_k=5, always_available=None, max_search_rounds=3, reranker=None, search="bm25")`

`AsyncOpenAIDehydratedClient`

`ToolIndex(tools, *, top_k=5, reranker=None, candidates=10)`

`JevIndex`, `JevReranker`, `Reranker`, `SearchIndex`