Is This Solana Token a Rug? A 4-Call Rug Check With Real Data

How do you check if a Solana token is a rug?

You check four separate ways a holder gets trapped. Can new supply be printed or your tokens frozen? Is the volume real? Can you actually sell? And who made this, and what else did they launch?

This page is the tool-led version. It runs those four checks as four calls and reads each payload field by field, including the fields that mislead. If you want the educational background on why each signal matters, read the Solana token due diligence checklist first. This article is what you do with real output.

All payloads below were pulled Oct 8, 2026, between 06:00 and 06:20 UTC through StalkChain's MCP.

I used two tokens on purpose. CAPYBARA is a launch that was a few hours old, so it gets the structural checks. E/ACC is about two weeks old with a $3.2M market cap, so it gets the volume and exit checks. In practice you would run all four calls on the same mint.

Call 1: What do the authorities, LP burn and top holders say?

The first call is stalkchain_token_onchain. It returns the on-chain safety card for a Solana mint.

stalkchain_token_onchain
  token: 3xrw3JKyaSYjzksYc8nrZE1kReQAxoHT3epi3P1mpZVf

The prompt you would type into Claude or ChatGPT: "Is this Solana token safe? Check mint authority, LP burn and top 10 holders."

{
  "symbol": "CAPYBARA",
  "holdersTotal": 3795,
  "lpBurnedPercent": 100,
  "mintAuthorityActive": false,
  "freezeAuthorityActive": false,
  "createdAt": "2026-10-08T03:47:18Z",
  "marketCapUsd": 319000,
  "marketCapBasis": "fully diluted",
  "liquidityUsd": 55510.1,
  "concentration": { "top10Percent": 11.38, "devPercent": 0 },
  "launch": { "sniperCount": 0, "insiderCount": 0 },
  "risk": {
    "score": 4,
    "rugged": false,
    "flags": [{ "name": "Top 10 Holders", "level": "danger" }]
  },
  "priceChangePercent": { "1h": -59.32, "2h": 10401.48, "24h": 10401.48 }
}

Pulled Oct 8, 2026, 06:00 to 06:20 UTC via StalkChain's MCP (stalkchain_token_onchain). Trimmed.

Same call over REST:

curl -G https://data.stalkchain.com/api/v1/tools/stalkchain_token_onchain \
  -H "Authorization: Bearer sc_YOUR_API_KEY" \
  -d token=3xrw3JKyaSYjzksYc8nrZE1kReQAxoHT3epi3P1mpZVf

Reading it field by field

• mintAuthorityActive: false and freezeAuthorityActive: false. Nobody can print more supply or freeze your wallet. These are the two cheapest and most important checks on Solana.
• lpBurnedPercent: 100. The liquidity tokens are burned, so the classic "pull the pool" rug is closed. It says nothing about how deep the pool is.
• concentration.top10Percent: 11.38. The ten largest holders own 11.38% of supply. That is low for a fresh launch. devPercent: 0 means the deployer wallet holds nothing right now.
• holdersTotal: 3,795. Total on-chain holders, not just people who use one app.
• liquidityUsd against marketCapUsd. $55,510 behind a $319,000 fully diluted value is thin. Liquidity is what a sell has to go through, so this ratio matters more than the market cap.

What not to trust in this payload

Three fields need a caveat.

The "danger" flag on Top 10 Holders fires at 11.38%, which is a low concentration, next to an overall risk score of 4. The flag severity looks over-tuned. Read the number, not the color.

priceChangePercent from 2h to 24h is identical (10401.48). The token was under a day old, so every longer window collapses to the same baseline. It is not a 24h gain. Ignore it.

sniperCount: 0 and insiderCount: 0 should not be read as "no snipers." On the same mint, the early-buyers tool flagged 87 bundled wallets, and 82 of the first 100 buyers had already exited. The two tools define terms differently.

For launch forensics, use the wallet-level first buyers list and the sniper guide, which you can verify transaction by transaction.

Call 2: Is the volume organic or wash traded?

The second call is stalkchain_token_quality. High volume makes a token look alive. This call splits real traders from the rest.

stalkchain_token_quality
  token: CbcyNo7m1amFWqEQm2m4PLv1UNvpcL3C1Ujm6AkzpKoU

Prompt: "Is the volume on E/ACC real or wash trading?"

{
  "symbol": "e/acc",
  "organicScore": 76.31,
  "organicScoreLabel": "medium",
  "holders": 18641,
  "marketCapUsd": 3233295.89,
  "liquidityUsd": 406105.98,
  "firstPoolAt": "2026-09-25T21:14:34Z",
  "audit": {
    "mintAuthorityDisabled": true,
    "freezeAuthorityDisabled": true,
    "topHoldersPercent": 13.08,
    "devBalancePercent": null,
    "tokensMintedByDev": 657
  },
  "organicVolumeShare": { "5m": 2.9, "1h": 4.8, "6h": 4.4, "24h": 5.4 },
  "traders24h": 2775
}

Pulled Oct 8, 2026, 06:00 to 06:20 UTC via StalkChain's MCP (stalkchain_token_quality). Trimmed.

Reading it field by field

• organicVolumeShare.24h: 5.4. Only 5.4% of the last day's volume looks organic. The other 94.6% looks like loops and bot churn. The 5-minute, 1-hour and 6-hour windows (2.9, 4.8 and 4.4) agree, so this is not a one-off spike.
• organicScore: 76.31, label "medium." This contradicts the line above it. A token with 5.4% organic volume getting a "medium" score tells you the score and the volume share measure different things. When they disagree, trust the volume share for the question "is this volume real," and treat the score as a loose headline.
• tokensMintedByDev: 657. The developer wallet has minted 657 tokens. That is a serial-launcher signal, and the "medium" label ignores it.
• devBalancePercent: null. Null means unknown, not zero. Do not read it as "dev holds nothing."
• traders24h: 2,775. Fewer than 3,000 traders against 18,641 holders means most holders are idle.

Wash volume does not make a token a rug. It makes it hard to read. Price discovery on a mostly fake tape is unreliable, and any volume-based screen you used to find the token is suspect.

Call 3: Can you actually sell it?

The third call is stalkchain_token_exit_check. A liquidity number is an opinion. An executable quote is a fact.

stalkchain_token_exit_check
  token: CbcyNo7m1amFWqEQm2m4PLv1UNvpcL3C1Ujm6AkzpKoU
  usd: 1000

Prompt: "If I sell $1,000 of E/ACC right now, what do I get and what is the slippage?"

{
  "symbol": "e/acc",
  "quoted": true,
  "sellUsd": 1000,
  "youWouldReceiveUsd": 996.34,
  "costOfExitUsd": 3.66,
  "priceImpactPercent": 0.366,
  "buyPriceImpactPercent": 1.34,
  "venues": ["Meteora DLMM", "ZeroFi"],
  "liquidityUsd": 406105.98
}

Pulled Oct 8, 2026, 06:00 to 06:20 UTC via StalkChain's MCP (stalkchain_token_exit_check).

Reading it field by field

• youWouldReceiveUsd: 996.34. A $1,000 sell returns $996.34 in USDC. The exit costs $3.66.
• priceImpactPercent: 0.366. Your sell moves the price 0.366%. That is clean for this size.
• buyPriceImpactPercent: 1.34. Buying the same size costs more than selling. The asymmetry is plausible with concentrated liquidity, but the payload does not explain it.
• venues. The route goes through Meteora DLMM and ZeroFi, so the liquidity is on real pools, not one wallet.

This is a snapshot, not a guarantee. It does not show your slippage tolerance or route hops, and it is stale the moment a whale moves. It also tests one size.

Run it at the size you actually plan to trade, and again at 10 times that size, because the failure case for thin pools is the larger order. For the logic behind sizing exits, see Solana liquidity removal and exit risk.

So E/ACC passes the exit test and fails the volume test. That mix is common. A token can be easy to sell and still be mostly bots.

Call 4: Who deployed it, and what else have they launched?

The fourth call is stalkchain_token_deployer. It resolves the wallet that created a mint and lists its other launches.

stalkchain_token_deployer
  token: 3xrw3JKyaSYjzksYc8nrZE1kReQAxoHT3epi3P1mpZVf

Prompt: "Who deployed this token, and has this dev launched others?"

The response is large, so here is the summary: the deployer wallet (dtrz…edXy) shows 493 tokens launched, 157 graduated, 490 still liquid, with 50 listed.

CAPYBARA is the first row, with $55,021 in liquidity, a $312,474 market cap and 100% LP burned. Most of the remaining rows sit between roughly $3,800 and $59,000 in market cap, and many carry identical liquidity and market cap values.

Pulled Oct 8, 2026, 06:00 to 06:20 UTC via StalkChain's MCP (stalkchain_token_deployer).

Is this a serial rugger?

Not necessarily, and this is the case where the obvious reading is wrong. 493 launches in a short period points to a launcher or factory wallet, a bot or platform address that deploys tokens for many users, not a person. Several rows share identical liquidity and market cap values, which points to templated launches.

So "this dev launched 493 tokens" is the wrong flag to raise. The better flags are:

• A wallet with hundreds of launches is infrastructure. Judge the specific token, not the deployer's record. A factory's history says little about whether the person who used it will rug.
• A graduation rate of about 32% (157 of 493) is a platform statistic, not a character reference.
• stillLiquid: 490 means little when most of those tokens hold $8,000 to $10,000 of liquidity.
• The list stops at 50 tokens. There is no pagination, so you see a sample, not all 493.
• There is no per-token outcome field. The tool does not say which launches rugged or died.

Compare that with the opposite case: a personal wallet with 20 launches, near-zero liquidity on 19 of them, and no graduations. That is the pattern the call is built to expose. Use it to separate the two.

What a combined KOL and safety read adds

The four calls above answer whether the token is trapped. They do not answer who is in it. That is where the tracked-trader layer helps.

On the same E/ACC mint and day, stalkchain_fomo_kol_holders found 100 tracked traders holding it, worth $649,498 together. That is only 0.54% of its holders, so treat it as a sample of notable wallets, not of the crowd.

stalkchain_fomo_kol_sell_pressure then showed 7 of 105 tracked holders had sold in the last 24 hours, with 5 fully out. At 6.67%, that sits below the tool's 10% alert threshold.

Put together, the E/ACC picture is: authorities disabled, a clean $1,000 exit, 94.6% of volume looking non-organic, and tracked traders mostly still holding rather than running for the door. That reads better than any single flag.

The contract is not the problem, the tape is noisy, and the smart wallets are not leaving yet.

It still has limits. Tracked holders are a small slice. They can be wrong, and some of them are losing money on this token. Treat their positions as context, not a signal to follow. For the trader side of the data, see Solana data for AI agents.

How do Birdeye, RugCheck and GMGN compare?

They are good tools, and for many people one of them is enough. Here is what each one does, as I read their public pages.

If you want a fast visual check before a trade, RugCheck and GMGN are quick and free to try. If you are building your own scanner in code, Birdeye's guide is a solid walkthrough with Python, TypeScript and cURL examples.

What this workflow adds is narrower: a real dollar quote for the exit on Solana, an organic-volume split next to a score that disagrees with it, and a deployer lookup that tells you when the wallet is a factory.

Everything is callable from an AI assistant over MCP, so the four calls become one question. The payload quirks above are the price of the raw output, and I would rather show them than hide them.

When should you walk away?

Use the four calls as a filter, not a verdict.

• Walk away: an active mint or freeze authority, liquidity that is not burned or locked and can be pulled, an exit quote that returns far less than you put in, or a personal deployer wallet with a trail of dead launches.
• Size down: very thin liquidity against market cap, top-10 concentration you cannot explain, or organic volume under about 10% when your thesis depends on activity.
• Keep looking: everything passes, but the token is hours old. Rugs are not only technical. Coordinated early buyers can exit on you, which is why the first-buyers check belongs next to these four.

For the full ten-check process, including social and funding signals, go back to the Solana token due diligence checklist.

Run it yourself

Connect the StalkChain MCP server to Claude, ChatGPT or Cursor and paste a mint. New accounts get $5 in free credit with no card, and data plans start at $29 a month for 1,500 calls (see API and MCP pricing).

Deployer, sniper and similar token forensics are Solana only. Developers can also work with the Solana data page and the Jupiter data page for the routing side.

Data, not financial advice. Nothing here tells you to buy or sell a token.

FAQ

What is the fastest way to check if a Solana token is a rug?

Check mint and freeze authority first, then LP burn, then run a sell quote at your intended size. Those three take under a minute and rule out the most common traps. Then look at who deployed it and whether the volume is real.

Does a 100% LP burn mean a token is safe?

No. A burned LP closes the "pull the pool" exit, but it says nothing about how deep the pool is, who holds the supply, or whether the volume is real. CAPYBARA had a 100% burn and only $55,510 in liquidity behind a $319,000 fully diluted value.

Is a deployer with hundreds of launches a serial rugger?

Not by itself. A wallet with 493 launches, like the one behind CAPYBARA, behaves like a launcher or factory address that deploys for many users. Judge the specific token, and look for a personal wallet with many dead launches and no graduations instead.

Why can a token have a medium organic score and 5.4% organic volume?

The headline score and the volume share are different measures. E/ACC scored 76.31 ("medium") while only 5.4% of 24h volume looked organic. When they disagree, use the volume share to judge whether activity is real.

Can I sell my position if the exit check passes?

The check is a snapshot of a real route at one size. E/ACC returned $996.34 on a $1,000 sell with 0.366% price impact at the time of the pull. Prices and pools move, so rerun it at your actual size right before you trade.

Is RugCheck or Birdeye better than this?

They solve overlapping problems with different formats. RugCheck is a quick web scanner, and Birdeye documents a five-check API workflow. This approach is for people who want the checks inside an AI assistant, with a dollar-based exit quote and the raw payloads to verify.