From antigravity-skills
Designs unambiguous agent tools with precise descriptions, consolidates toolsets, and optimizes interfaces for reliable AI agent performance.
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Design every tool as a contract between a deterministic system and a non-deterministic agent. Unlike human-facing APIs, agent-facing tools must make the contract unambiguous through the description alone -- agents infer intent from descriptions and generate calls that must match expected formats. Every ambiguity becomes a potential failure mode that no amount of prompt engineering can fix.
Designs unambiguous agent tools with precise descriptions, consolidates toolsets, and optimizes interfaces for reliable AI agent performance.
Designs effective tools for AI agents, covering descriptions, namespacing, complexity reduction, and contracts. Use when creating, optimizing, debugging, or evaluating agent toolsets.
Designs tools for AI agents with effective principles like clear descriptions and architectural reduction patterns. Use for creating new tools, debugging failures, or optimizing toolsets.
Share bugs, ideas, or general feedback.
Design every tool as a contract between a deterministic system and a non-deterministic agent. Unlike human-facing APIs, agent-facing tools must make the contract unambiguous through the description alone -- agents infer intent from descriptions and generate calls that must match expected formats. Every ambiguity becomes a potential failure mode that no amount of prompt engineering can fix.
Activate this skill when:
Design tools around the consolidation principle: if a human engineer cannot definitively say which tool should be used in a given situation, an agent cannot be expected to do better. Reduce the tool set until each tool has one unambiguous purpose, because agents select tools by comparing descriptions and any overlap introduces selection errors.
Treat every tool description as prompt engineering that shapes agent behavior. The description is not documentation for humans -- it is injected into the agent's context and directly steers reasoning. Write descriptions that answer what the tool does, when to use it, and what it returns, because these three questions are exactly what agents evaluate during tool selection.
Tools as Contracts Design each tool as a self-contained contract. When humans call APIs, they read docs, understand conventions, and make appropriate requests. Agents must infer the entire contract from a single description block. Make the contract unambiguous by including format examples, expected patterns, and explicit constraints. Omit nothing that a caller needs to know, because agents cannot ask clarifying questions before making a call.
Tool Description as Prompt Write tool descriptions knowing they load directly into agent context and collectively steer behavior. A vague description like "Search the database" with cryptic parameter names forces the agent to guess -- and guessing produces incorrect calls. Instead, include usage context, parameter format examples, and sensible defaults. Every word in the description either helps or hurts tool selection accuracy.
Namespacing and Organization
Namespace tools under common prefixes as the collection grows, because agents benefit from hierarchical grouping. When an agent needs database operations, it routes to the db_* namespace; when it needs web interactions, it routes to web_*. Without namespacing, agents must evaluate every tool in a flat list, which degrades selection accuracy as the count grows.
Single Comprehensive Tools
Build single comprehensive tools instead of multiple narrow tools that overlap. Rather than implementing list_users, list_events, and create_event separately, implement schedule_event that finds availability and schedules in one call. The comprehensive tool handles the full workflow internally, removing the agent's burden of chaining calls in the correct order.
Why Consolidation Works Apply consolidation because agents have limited context and attention. Each tool in the collection competes for attention during tool selection, each description consumes context budget tokens, and overlapping functionality creates ambiguity. Consolidation eliminates redundant descriptions, removes selection ambiguity, and shrinks the effective tool set. Vercel demonstrated this principle by reducing their agent from 17 specialized tools to 2 general-purpose tools and achieving better performance -- fewer tools meant less confusion and more reliable tool selection.
When Not to Consolidate Keep tools separate when they have fundamentally different behaviors, serve different contexts, or must be callable independently. Over-consolidation creates a different problem: a single tool with too many parameters and modes becomes hard for agents to parameterize correctly.
Push the consolidation principle to its logical extreme by removing most specialized tools in favor of primitive, general-purpose capabilities. Production evidence shows this approach can outperform sophisticated multi-tool architectures.
The File System Agent Pattern Provide direct file system access through a single command execution tool instead of building custom tools for data exploration, schema lookup, and query validation. The agent uses standard Unix utilities (grep, cat, find, ls) to explore and operate on the system. This works because file systems are a proven abstraction that models understand deeply, standard tools have predictable behavior, agents can chain primitives flexibly rather than being constrained to predefined workflows, and good documentation in files replaces summarization tools.
When Reduction Outperforms Complexity Choose reduction when the data layer is well-documented and consistently structured, the model has sufficient reasoning capability, specialized tools were constraining rather than enabling the model, or more time is spent maintaining scaffolding than improving outcomes. Avoid reduction when underlying data is messy or poorly documented, the domain requires specialized knowledge the model lacks, safety constraints must limit agent actions, or operations genuinely benefit from structured workflows.
Build for Future Models Design minimal architectures that benefit from model improvements rather than sophisticated architectures that lock in current limitations. Ask whether each tool enables new capabilities or constrains reasoning the model could handle on its own -- tools built as "guardrails" often become liabilities as models improve.
See Architectural Reduction Case Study for production evidence.
Description Structure Structure every tool description to answer four questions:
Default Parameter Selection Set defaults to reflect common use cases. Defaults reduce agent burden by eliminating unnecessary parameter specification and prevent errors from omitted parameters. Choose defaults that produce useful results without requiring the agent to understand every option.
Offer response format options (concise vs. detailed) because tool response size significantly impacts context usage. Concise format returns essential fields only, suitable for confirmations. Detailed format returns complete objects, suitable when full context drives decisions. Document when to use each format in the tool description so agents learn to select appropriately.
Design error messages for two audiences: developers debugging issues and agents recovering from failures. For agents, every error message must be actionable -- it must state what went wrong and how to correct it. Include retry guidance for retryable errors, corrected format examples for input errors, and specific missing fields for incomplete requests. An error that says only "failed" provides zero recovery signal.
Establish a consistent schema across all tools. Use verb-noun pattern for tool names (get_customer, create_order), consistent parameter names across tools (always customer_id, never sometimes id and sometimes identifier), and consistent return field names. Consistency reduces the cognitive load on agents and improves cross-tool generalization.
Limit tool collections to 10-20 tools for most applications, because research shows description overlap causes model confusion and more tools do not always lead to better outcomes. When more tools are genuinely needed, use namespacing to create logical groupings. Implement selection mechanisms: tool grouping by domain, example-based selection hints, and umbrella tools that route to specialized sub-tools.
Always use fully qualified tool names with MCP (Model Context Protocol) to avoid "tool not found" errors.
Format: ServerName:tool_name
# Correct: Fully qualified names
"Use the BigQuery:bigquery_schema tool to retrieve table schemas."
"Use the GitHub:create_issue tool to create issues."
# Incorrect: Unqualified names
"Use the bigquery_schema tool..." # May fail with multiple servers
Without the server prefix, agents may fail to locate tools when multiple MCP servers are available. Establish naming conventions that include server context in all tool references.
Feed observed tool failures back to an agent to diagnose issues and improve descriptions. Production testing shows this approach achieves 40% reduction in task completion time by helping future agents avoid mistakes.
The Tool-Testing Agent Pattern:
def optimize_tool_description(tool_spec, failure_examples):
"""
Use an agent to analyze tool failures and improve descriptions.
Process:
1. Agent attempts to use tool across diverse tasks
2. Collect failure modes and friction points
3. Agent analyzes failures and proposes improvements
4. Test improved descriptions against same tasks
"""
prompt = f"""
Analyze this tool specification and the observed failures.
Tool: {tool_spec}
Failures observed:
{failure_examples}
Identify:
1. Why agents are failing with this tool
2. What information is missing from the description
3. What ambiguities cause incorrect usage
Propose an improved tool description that addresses these issues.
"""
return get_agent_response(prompt)
This creates a feedback loop: agents using tools generate failure data, which agents then use to improve tool descriptions, which reduces future failures.
Evaluate tool designs against five criteria: unambiguity, completeness, recoverability, efficiency, and consistency. Test by presenting representative agent requests and evaluating the resulting tool calls against expected behavior.
When designing tool collections:
Example 1: Well-Designed Tool
def get_customer(customer_id: str, format: str = "concise"):
"""
Retrieve customer information by ID.
Use when:
- User asks about specific customer details
- Need customer context for decision-making
- Verifying customer identity
Args:
customer_id: Format "CUST-######" (e.g., "CUST-000001")
format: "concise" for key fields, "detailed" for complete record
Returns:
Customer object with requested fields
Errors:
NOT_FOUND: Customer ID not found
INVALID_FORMAT: ID must match CUST-###### pattern
"""
Example 2: Poor Tool Design
This example demonstrates several tool design anti-patterns:
def search(query):
"""Search the database."""
pass
Problems with this design:
Failure modes:
x, val, or param1 force agents to guess meaning. Use descriptive names that convey purpose without reading further documentation.id in one tool, identifier in another, and customer_id in a third creates confusion. Standardize parameter names across the entire tool collection.search), agents cannot disambiguate. Always use fully qualified ServerName:tool_name format and audit for collisions when adding new providers.options object.This skill connects to:
Internal references:
Related skills in this collection:
External resources:
Created: 2025-12-20 Last Updated: 2026-03-17 Author: Agent Skills for Context Engineering Contributors Version: 2.0.0