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Find Payable Counterparty Agents

find_agents
Read-onlyIdempotent

Counterparty discovery: "which agents can do X and are safe to pay?" Returns the top 3 ranked candidates with liveness (30-day Ghost Index rule), trust tier, verified payment rails (x402/MCP/ERC-8004), scores, and endpoints, plus the total match count. The full ranked list (up to 50) is at https://agentcrush.xyz/api/agents/find/full — $0.05 via x402 on Base, or free with an AgentCrush Pro key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesCapability keyword (required), e.g. "trading", "wallet risk", "code review".
aliveNotrue = only agents alive per the 30-day liveness rule.
railsNoPayment rail filter, e.g. "x402".
categoryNoRestrict to one AgentCrush category.
min_tierNoExclude indexed-only agents.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
filtersNo
candidatesNo
full_resultsNoPointer to the paid full-list endpoint when more matches exist.
total_matchesNo

TDQS

A4.5/5.0
Behavior5/5

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

Even with annotations declaring readOnlyHint, openWorldHint, and idempotentHint, the description adds substantial behavioral context: the 30-day Ghost Index liveness rule, trust tiers, verified payment rails (x402/MCP/ERC-8004), top-3 ranking, total match count, and the paid endpoint for the full list. This goes well beyond the annotations and gives the agent a clear model of behavior.

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 two sentences: the first states purpose and return contents, the second gives the full-list URL and pricing. Every sentence earns its place, and the structure front-loads the most important information.

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 rich annotations, 100% schema coverage, and an output schema, the description is complete: it covers ranking criteria, safety indicators, filterable dimensions, output scope, and a follow-up endpoint. No critical usage context is missing for a read-only discovery tool.

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

Parameters3/5

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

Schema description coverage for all 5 parameters is 100%, so the baseline is 3. The description repeats some concepts (liveness, rails) but does not add new parameter-level meaning beyond the schema's own property descriptions. It neither hurts nor enhances parameter understanding materially.

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 opens with a clear, specific purpose: "Counterparty discovery: 'which agents can do X and are safe to pay?'" and details exactly what is returned (top 3 ranked candidates, liveness, trust tier, payment rails, scores, endpoints). This distinguishes it from sibling tools like search_agents by emphasizing safe-to-pay ranking and verified rails.

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

Usage Guidelines4/5

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

The description establishes clear context for when to use this tool (counterparty discovery for payable agents) and includes practical details like the full list link and cost. However, it does not explicitly state when not to use it or name alternatives (e.g., compare_agents, verify_counterparty), so it stops short of full exclusion 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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool serves a clearly distinct purpose: comparison, discovery, details, history, trust, rankings, ecosystem summaries, methodology, movers, categories, search, and verification. There is no meaningful overlap that could cause an agent to select the wrong tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., compare_agents, find_agents, get_agent_trust, verify_counterparty). The pattern is uniform and predictable across the entire set.

Tool Count5/5

14 tools is within the ideal 3-15 range and each tool maps to a distinct query type for the AgentCrush domain. The scope feels well-covered without unnecessary bloat.

Completeness4/5

The surface covers discovery, detail, history, trust, comparison, ranking, and ecosystem-level analytics. The only notable gap is a lack of a direct 'list all agents' tool; the full ranked list is provided via external URL rather than a first-class tool, but this is a minor limitation given find_agents and search_agents cover discovery.