Tool Search

Progressive tool disclosure for agents with many MCP/plugin tools

Tool Search enables progressive disclosure of tools when agents have many MCP or plugin tools, reducing context overhead and improving performance.

python

from praisonaiagents import Agent

agent = Agent(
    name="MCP Agent",
    instructions="You can search Slack, Google Drive, and GitHub",
    tool_search=True,
)
agent.start("Find the design review thread on Slack")

The user asks about Slack; tool_search discloses only the tools needed so large MCP catalogues stay out of context.

Diagram

Quick Start

Simple Usage

Enable Tool Search with auto mode to activate progressive disclosure when needed:

python

from praisonaiagents import Agent

agent = Agent(
    name="MCP Agent",
    instructions="You can search Slack, Google Drive, and GitHub",
    tool_search=True  # Auto mode - activates when tool schemas exceed threshold
)

agent.start("Find the design review thread on Slack")

With Configuration

Use ToolSearchConfig for fine-grained control:

python

from praisonaiagents import Agent, ToolSearchConfig

agent = Agent(
    name="Heavy MCP Agent", 
    instructions="You have access to many enterprise tools",
    tool_search=ToolSearchConfig(
        enabled="auto",
        threshold_pct=15.0,  # Activate when tools use >15% of context
        search_default_limit=8,
        max_search_limit=25
    )
)

agent.start("Search for quarterly reports in our systems")

How It Works

Diagram

When bridge mode activates, the model sees three lightweight tools instead of potentially hundreds of MCP tool schemas:

Bridge Tool	Purpose
`tool_search(query, limit)`	BM25 search over deferred tool names and descriptions
`tool_describe(tool_name)`	Returns full OpenAI function schema for the named tool
`tool_call(tool_name, tool_args)`	Executes the real tool with unwrapping for traces/hooks

When to Use It

Diagram

Use Tool Search when:

Your agent connects to 10+ MCP servers
You have 50+ tools from plugins/integrations
Context window usage is a concern
LLM responses are slow due to large tool schemas

Skip Tool Search when:

You have fewer than 10 tools total
All tools are frequently used core operations
Tool discovery latency is critical

Configuration Options

Option	Type	Default	Description
`enabled`	`Union[bool, str]`	`"auto"`	Mode: `"auto"`, `"on"`, `"off"`, `True`, `False`. Auto only activates when deferrable schema tokens cross threshold.
`threshold_pct`	`float`	`10.0`	Percentage of model context window that deferrable schemas must occupy before auto mode kicks in.
`search_default_limit`	`int`	`5`	Default number of results returned by `tool_search`.
`max_search_limit`	`int`	`20`	Upper cap on results per `tool_search` call.
`core_tools`	`Optional[FrozenSet[str]]`	`None`	Override the default set of tools that never defer.

Precedence Ladder

Tool Search supports multiple configuration levels:

python

# Level 1: Bool (simplest)
Agent(tool_search=True)              # auto mode
Agent(tool_search=False)             # disabled (default)

# Level 2: String shorthand
Agent(tool_search="auto")            # explicit auto
Agent(tool_search="on")              # always bridge
Agent(tool_search="off")             # disabled

# Level 3: Dict
Agent(tool_search={"enabled": "auto", "threshold_pct": 15})

# Level 4: Config class (full control)
from praisonaiagents import Agent, ToolSearchConfig
Agent(tool_search=ToolSearchConfig(
    enabled="auto",
    threshold_pct=10,
    search_default_limit=5,
    max_search_limit=20,
))

Core vs Deferrable Tools

Diagram

Core Tools (Never Defer)

These 16 essential tools always remain visible:

Category	Tools
File operations	`read_file`, `write_file`, `list_files`, `get_file_info`, `copy_file`, `move_file`, `delete_file`
Shell operations	`execute_command`, `list_processes`, `kill_process`, `get_system_info`
Web operations	`search_web`, `web_search`, `internet_search`, `web_crawl`, `crawl_web`
Scheduling	`schedule_add`, `schedule_list`, `schedule_remove`
Memory	`store_memory`, `search_memory`
Clarify	`clarify`

Deferrable Tools

MCP tools: Automatically marked with __praisonai_deferrable__ = True
Custom tools: Add deferrable: True to function metadata
MCP-prefixed: Tools starting with mcp_

Common Patterns

MCP-Heavy Agent

For agents with many MCP servers:

python

from praisonaiagents import Agent

agent = Agent(
    name="Enterprise Agent",
    instructions="Access Slack, Jira, GitHub, Google Drive, and Salesforce",
    tool_search=True,  # Auto mode handles the complexity
    tools=[slack_mcp, jira_mcp, github_mcp, gdrive_mcp, salesforce_mcp]
)

Force-On for Testing

Always use bridge mode for testing or high-tool-count scenarios:

python

agent = Agent(
    name="Test Agent",
    instructions="Testing tool search with all MCP tools",
    tool_search="on"  # Always bridge, regardless of count
)

Custom Threshold

Adjust when bridge mode activates:

python

from praisonaiagents import Agent, ToolSearchConfig

agent = Agent(
    name="Conservative Agent", 
    instructions="More aggressive tool deferral",
    tool_search=ToolSearchConfig(
        enabled="auto",
        threshold_pct=5.0,  # Activate with less tool overhead
        search_default_limit=3  # Fewer search results
    )
)

Best Practices

When to Enable Tool Search

Enable Tool Search when your agent has 10+ tools, especially from MCP servers. The auto mode intelligently activates only when tool schemas would consume significant context window space. For agents with 3–10 tools, the overhead isn’t worth the complexity.

Choosing the Right Threshold

The default 10% threshold works well for most scenarios. Lower it to 5-7% for aggressive context saving, or raise it to 15-20% if you prefer fewer bridge interactions. Monitor your context usage and adjust based on your model’s window size.

Writing Better Search Queries

More capable models (GPT-4, Claude-3.5-Sonnet) write better tool search queries. They understand semantic relationships and can find tools by functionality rather than just name matching. Smaller models may need more explicit tool naming or descriptions.

What NOT to Enable For

Don’t use Tool Search for agents with fewer than 10 tools, or when all tools are frequently used core operations. The 1-2 extra round trips for cold tool access aren’t worth it for small, stable toolsets.

Trade-offs

Benefits:

Reduces context window usage by 60-80% for tool-heavy agents
Improves LLM response speed with fewer schemas to process
Scales to hundreds of MCP tools without context bloat
Auto mode provides zero overhead when not needed

Costs:

Cold tool access requires 1-2 extra round trips (search → describe → call)
tool_describe results enter conversation history, growing context
Bridge mode invalidates existing tool schemas between sessions
Smaller models may write less effective search queries

Load MCP Tools

Connect MCP servers and load their tools

Allowed Tools

Filter and control tool availability

Tool Search

Quick Start

How It Works

When to Use It

Configuration Options

Precedence Ladder

Core vs Deferrable Tools

Core Tools (Never Defer)

Deferrable Tools

Common Patterns

MCP-Heavy Agent

Force-On for Testing

Custom Threshold

Best Practices

Trade-offs

Related