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search_agents

Search for AI agents by capability, provider, or reputation to find suitable agents for task delegation.

Instructions

Find AI agents by capability, provider, or minimum reputation score.

Use this to discover available agents for a task before delegation.
Results are sorted by reputation score (highest first).
Combine filters to narrow results.

Use get_agent_info when you already have a specific DID.
Use check_reputation or check_trust to evaluate a found agent.

Read-only — does not modify any data.

Args:
    capability: Filter by published capability. Examples:
                "code_review", "security_audit", "translation". Empty for all.
    provider: Filter by LLM provider. Examples: "anthropic", "openai". Empty for all.
    min_reputation: Minimum reputation score (0.0-1.0). Default 0.0 returns all.
    limit: Maximum number of results (1-100). Default 10.

Returns:
    JSON list of matching agents with DID, display_name, capabilities,
    provider, and reputation score. Returns empty list if no matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
capabilityNoFilter by capability. Examples: code_review, security_audit, translation, data_analysis. Empty returns all
providerNoFilter by LLM provider. Examples: anthropic, openai, google, mistral. Empty returns all
min_reputationNoMinimum reputation score 0.0-1.0. Set 0.5+ to exclude unproven agents. Default: 0.0
limitNoMaximum results to return, 1-100. Default: 10

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description correctly declares 'Read-only — does not modify any data.' It also explains result sorting by reputation score and what the return format includes. However, it does not mention pagination behavior (e.g., if there are more than limit results) or tie-breaking on sorting, which would be relevant for completeness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: purpose, usage context, alternative tools, read-only note, parameter explanations with examples, and return format. Every sentence adds value without redundancy. It is front-loaded with the main action and usage intent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 4 optional parameters and no required ones, the description fully covers parameter usage, output format, and behavior. It explains filtering logic, defaults, sorting, and provides usage context. The return format is described explicitly, and the presence of an output schema (as per context signals) is complemented by the description. No gaps remain for an agent to understand usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for capability and provider, specifying ranges and defaults for min_reputation (0.0-1.0) and limit (1-100), and explaining that empty values return all results. This goes beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Find AI agents by capability, provider, or minimum reputation score' and distinguishes from siblings by specifying when to use alternatives (get_agent_info, check_reputation, check_trust). This provides a clear verb+resource combination and differentiates from other tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context: 'Use this to discover available agents for a task before delegation.' It also tells when not to use it and suggests alternatives: 'Use get_agent_info when you already have a specific DID. Use check_reputation or check_trust to evaluate a found agent.' This provides clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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