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agentsvc-mcp

agentsvc-mcp

by jakobautomation
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

token_simulate

Replays an on-chain token buy and sell to detect honeypots: returns buy/sell success, taxes, reason, and estimated round-trip cost before you trade.

Instructions

Token Buy and Sell Simulation (Honeypot Test) — Replays a real buy and sell of the token on chain state and tells you if you can get out again: buy_succeeded, sell_succeeded, buy and sell tax in percent, honeypot reason, estimated round-trip cost in percent and USD for amount_usd, plus the pools used. Use before buying an unknown token or in a trading bot. EVM chains are simulated; Solana returns status not_simulated with authority and pool checks. No liquidity returns status no_liquidity. Cost excludes LP fees and gas. Cost: $0.012 USDC per call. Latency p99: 15000ms.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "token": "<token>"
    },
    "name": "token_simulate"
  }
}

Input Schema

NameRequiredDescriptionDefault
chainNobase (default), ethereum, bsc, arbitrum, polygon, avalanche or optimism
tokenYesToken contract address (0x...)
amount_usdNoTrade size in USD, 1 to 100000

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
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 does so well: it lists the exact return fields (buy_succeeded, sell_succeeded, taxes, honeypot reason, round-trip cost, pools), discloses chain coverage limits (EVM simulated, Solana not), cost exclusions (LP fees and gas), price ($0.012 USDC) and p99 latency (15000ms).

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?

Front-loads the core purpose in the first clause, then packs outputs, usage, edge cases, and cost into a dense but skimmable block. Every sentence carries information, though the run-on list of outputs and caveats is slightly crowded.

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?

There is no output schema, so the description must enumerate return values and edge-case statuses, which it does comprehensively, along with cost and latency. An agent has everything needed to decide and call correctly.

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 is 100%, so the schema already documents chain, token, and amount_usd (including enum-like chain list and the 1-100000 range). The description adds only incidental context by referencing amount_usd in the cost output, so the baseline 3 is appropriate.

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?

States a specific verb and resource (replays a real buy and sell of the token on chain state) plus the goal (honeypot detection / whether you can exit). It clearly differentiates from siblings like token_security, token_precheck, and token_liquidity by being a simulation rather than a static scan.

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?

Explicitly says when to use it: 'Use before buying an unknown token or in a trading bot.' It also notes Solana returns not_simulated and no liquidity returns no_liquidity, which bounds applicability. It does not name a specific alternative tool to use instead, so it falls short of a 5.

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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