toolscope 0.1.0

ToolScope: per-prompt automatic tool selection for LLM tool calling.

ToolScope

ToolScope is a lightweight, production-ready Tool RAG library for agentic systems.

It solves a core scalability problem in tool-using agents:

As the number of tools grows, LLMs become worse at selecting the right one.

ToolScope fixes this by filtering tools per turn using semantic retrieval — without changing how the model interacts with tools.

No meta-tools.
No retrievers exposed to the model.
No framework lock-in.

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Why ToolScope?

When agents see hundreds of tools:

• tool selection accuracy drops
• context windows overflow
• latency increases
• small models break first

ToolScope:

• indexes tool descriptions in a vector database
• retrieves only the most relevant tools per prompt
• hands those tools to the model exactly as before

The model doesn’t know ToolScope exists — it just performs better.

Use ToolScope when:

• you have dozens to hundreds of tools
• tools come from MCP servers or registries
• you want to keep using standard agent frameworks
• you want predictable, debuggable behavior

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Quickstart

1. Install

pip install toolscope

(or from source)

pip install -e .

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2. Minimal filtering

import toolscope

class TinyEmbedder:
    def embed_texts(self, texts):
        return [[len(t) % 97, t.count("jira"), sum(map(ord, t)) % 101] for t in texts]

tools = [
    {"name": "jira_create_issue", "description": "Create a Jira issue", "inputSchema": {}},
    {"name": "confluence_search", "description": "Search Confluence pages", "inputSchema": {}},
]

filtered = toolscope.filter(
    messages=[{"role": "user", "content": "Create a Jira ticket"}],
    tools=tools,
    embedder=TinyEmbedder(),
    k=1,
)

print(filtered)  # same tools, fewer of them

or, with an embedding configuration (requires sentence-transformers):

import toolscope

embedding_config = toolscope.EmbeddingConfig(
    provider="sentence-transformers",
    model="sentence-transformers/all-MiniLM-L6-v2",
    allow_download=False,
)

tools = [
    {"name": "jira_create_issue", "description": "Create a Jira issue", "inputSchema": {}},
    {"name": "confluence_search", "description": "Search Confluence pages", "inputSchema": {}},
]

filtered = toolscope.filter(
    messages=[{"role": "user", "content": "Create a Jira ticket"}],
    tools=tools,
    embedding=embedding_config,
    k=1,
)

print(filtered)  # same tools, fewer of them

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3. Indexed (stateful) usage

idx = toolscope.index(
    tools,
    embedder=TinyEmbedder(),
)

filtered = idx.filter(
    messages="Create a Jira ticket",
    k=1,
)

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Core Concepts and Features

Canonical tools

ToolScope normalizes tools from many schemas into a canonical form:

• name
• description
• input schema
• tags
• fingerprint

Original tool objects are preserved and returned unchanged.

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Embeddings

ToolScope supports pluggable embedding backends.

Option A: Provide your own embedder (recommended default)

class MyEmbedder:
    def embed_texts(self, texts): ...

Option B: Use EmbeddingConfig (HTTP, OpenAI-style, etc.)

toolscope.EmbeddingConfig(
    provider="http",
    endpoint="http://localhost:8000/embed",
    model="my-embedding-model",
)

ToolScope never downloads models behind your back.

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Tool text control

You control what text is embedded:

toolscope.ToolTextConfig(
    use_name=True,
    use_description=True,
    use_schema=False,
    truncate=256,
)

Defaults (battle-tested):

• name + description only
• truncate to 256 chars
• no preprocessing

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Advanced Features

✅ Allow / deny filters

idx.filter(
    messages,
    allow_tags=["jira"],
    deny_tags=["dangerous"],
)

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🔁 Sticky toolsets (multi-turn sessions)

Reuse tools across turns when the query stays similar:

toolscope.StickySessionConfig(
    enabled=True,
    similarity_threshold_reuse=0.95,
    similarity_threshold_refresh=0.8,
    sticky_keep=2,
)

This reduces latency and improves consistency.

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🧠 Reranking

Boost retrieval quality using a cross-encoder:

toolscope.RerankingConfig(
    model="cross-encoder/ms-marco-MiniLM-L-6-v2",
    pool_size=20,
)

Not enabled by default — you opt in explicitly.

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📊 Observability

Inspect what ToolScope is doing:

tools, trace = idx.filter_with_trace(messages)
print(trace)

Includes:

• candidate counts
• timings
• allow/deny decisions
• reranking effects

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Backends

In-memory (default)

Fast, simple, zero dependencies.

toolscope.MemoryBackend()

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Milvus Lite

Persistent, scalable local vector DB:

toolscope.MilvusLiteBackend(path="./toolscope.db")

ToolScope is backend-agnostic; more vector DBs can be added.

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Adapters (Plug & Play)

ToolScope integrates cleanly with popular agent stacks.

LangChain / LangGraph

• full agent loops
• per-turn tool filtering
• middleware-based integration

from toolscope.adapters.langchain import (
    ToolSelector,
    make_toolscope_tool_selection_middleware,
)

See:

examples/langchain/

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FastMCP

• drop-in MCP client wrapper
• supports multi-server clients
• reacts to tools/list_changed notifications

from toolscope.adapters.fastmcp import ToolScopeFastMCPClient

See:

examples/fastmcp/

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License

This project is licensed under the Apache License 2.0.