pons-mcp

pons_scan_interesting

Identifies promising Pons v2 launches by scoring on-chain traction: unique buyers, curve ETH, buy/sell mix, age, serial-deployer filter. Sorted by score descending; no LLM cost.

Instructions

Score recent Pons v2 launches for traction using on-chain data only (unique buyers excluding deployer, ETH in the curve, buy/sell mix, age, serial-deployer filter). No LLM, no spend. Sorted by score descending.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "pons_scan_interesting"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Max results, newest first (default 20, max 100)
`lookbackBlocks`	No	Blocks to look back from latest (default 50000 ≈ 1.4h at ~10 blocks/s; max 500000)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Sep 15, 2026

TDQS

A3.9/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and delivers: it states the data source (on-chain only), the scoring criteria (unique buyers excluding deployer, ETH in curve, buy/sell mix, age), the serial-deployer filter, and the no-LLM/no-spend constraints. It also discloses the sort order. This is unusually transparent about how the tool behaves.

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

Conciseness4/5

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

The definition is a single information-dense sentence with the core purpose front-loaded. The parenthetical metric list packs many details together and could be reformatted for readability, but every clause earns its place.

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

Completeness4/5

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

For a simple two-parameter scan tool with no output schema, the description covers what it computes, on what data, and how results are ordered. It does not describe the return payload shape, which would help, but the absence of an output schema lowers that burden.

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?

The input schema already describes both parameters completely, including limits and defaults, so the description does not need to repeat them. The description adds only high-level context, which is adequate given 100% schema coverage.

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

Purpose4/5

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

The description opens with a specific action ('Score') and a clear resource ('recent Pons v2 launches'), then lists the traction metrics used. This makes the purpose immediately understandable, but it does not explicitly differentiate it from sibling tools such as pons_recent_launches or pons_creator_launches.

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

Usage Guidelines3/5

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

The description implies the tool is for screening recent launches via on-chain metrics, and 'No LLM, no spend' hints at a cost-sensitive use case. However, it gives no explicit guidance on when to choose this tool over the many related Pons siblings.

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