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RiskDataApi — Insider Cluster Detection for Solana Tokens

by menantonio83-hue
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
TypeScript
Remote

RiskDataApi — scanners count wallets, we trace who funded them

tnt-house MCP server

MCP Marketplace

One call returns the wallets that share a first funder — the cluster a scanner shows you as 20 unrelated holders. Plus safety score 0–100, honeypot and LP lock.

Connect with no key. MCP: https://www.tnt-audit.com/api/mcp Paste that URL into Glama Inspector, Claude, or Cursor and call check_token_risk. 5 calls/day, no email.

After that: email key 15/day, x402 at $0.02/call with no account, or a $45/5000-call subscription for volume. Humans on the site get 3 free checks with no signup at tnt-audit.com/risk-api.

This repository hosts a remote Model Context Protocol (MCP) server exposing the RiskDataApi engine as tools for AI agents (Claude, Cursor, and any other MCP-compatible client). See the MCP Server section below.

Getting Started (3 steps)

  1. Try it now, no signup — no API key needed for your first 5 MCP calls/day (no signup at all). Three ways, pick whichever is easiest:

    • ⚡ Glama's MCP Inspector (fastest, zero setup, real MCP call) — go to glama.ai/mcp/inspector, paste https://tnt-audit.com/api/mcp as the server URL, and call check_token_risk right in your browser — no install needed.

    • Claude Desktop / Cursor — paste this into your MCP config, no key required:

      {
  "mcpServers": {
    "tnt-risk-data-api": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://tnt-audit.com/api/mcp"]
    }
  }
}

    • Browser, raw REST preview — open a live example directly, no install, no signup. ⚠️ This is a REST preview of the underlying data, not the MCP tool itself — for the real MCP tool, use the Inspector above.

  2. Want more? — get a free API key in seconds at tnt-audit.com/risk-api (email only, no card).

  3. Need higher volume? — on the same page, paste your key into the pricing section to pay per-call, by subscription, or via x402 (autonomous agents, no key at all).

Related MCP server: Rug Munch Intelligence

Tech Stack

  • Next.js 14 (App Router) + React 18 + TypeScript

  • Tailwind CSS

  • Supabase (auth, billing, key storage)

  • Helius (Solana RPC) + RugCheck + DexScreener (market/risk data)

  • Vercel (hosting, cron via QStash)

MCP Server — Risk-Data API

This repository includes a remote Model Context Protocol (MCP) server, built with the official @modelcontextprotocol/sdk (TypeScript), exposing Solana token risk data as tools for AI agents.

Tools

Registered via server.registerTool(...):

Tool

Description

check_token_risk

Safety score, insider wallet cluster detection, mint/freeze authority, holder concentration, live price/liquidity/volume for one Solana token mint

check_token_risk_batch

Same as above for up to 25 mints in one call

get_token_risk_history

Hourly historical risk/price data for a mint over up to 90 days

Full tool schemas and input parameters: app/api/mcp/README.md.

Local Development

npm install
npm run dev

Open http://localhost:3000

Deployment

Vercel-ready. Connected to this repo's main branch for auto-deploys.

Links: X @RiskDataApiSol | Risk-Data API | Telegram

Available Tools

3 tools
check_token_riskCheck Solana token riskAInspect

Returns a 0-100 safety score, on-chain insider wallet clusters (wallets sharing a first funder), mint/freeze authority status, holder concentration, and live price/liquidity/volume for a single Solana token mint. Use before recommending or executing a trade on any Solana token. 5 free MCP calls/day, no signup. Then email key or x402.

ParametersJSON Schema
NameRequiredDescriptionDefault
mintYesThe Solana token mint address to check

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It does this well by specifying the 0-100 score range, the data dimensions returned, and the quota/auth model ('5 free MCP calls/day, no signup. Then email key or x402'). It leaves failure modes or interpretation thresholds unstated, but the core behavior is transparent.

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?

Two sentences carry a substantial amount of useful information: output summary, call trigger, quota, and auth next step. The phrase 'Then email key or x402' is slightly cryptic but not bloated. Structure is efficient and front-loaded.

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?

Given no output schema and no annotations, the description supplies the key return categories and the invocation context, which is strong coverage for a single-parameter read-style tool. It could go further by explaining how to interpret the 0-100 score or what happens on invalid mints, but it is not incomplete enough to warrant a lower score.

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 coverage for the only parameter, mint, is 100%, so the schema already defines it as 'The Solana token mint address to check.' The description reinforces 'single Solana token mint' but adds no new format, validation, or usage nuance. Baseline 3 applies.

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 uses a specific verb ('Returns') tied to a concrete resource ('a single Solana token mint') and enumerates the outputs: safety score, insider clusters, authority status, holder concentration, and price/liquidity/volume. It also implicitly differentiates from siblings (batch, history) via the 'single token' scoping.

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 gives a clear trigger: 'Use before recommending or executing a trade on any Solana token.' It does not explicitly name when to choose batch or history alternatives, but the 'single token' phrasing signals the boundary. There are no misleading exclusions.

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

check_token_risk_batchCheck risk for multiple Solana tokens at onceAInspect

Same as check_token_risk but for up to 25 mints in one call — N mints charged as N calls, same per-call economics as check_token_risk, no bulk discount. All-or-nothing: if the batch can't be fully covered by remaining quota/credit, the whole batch is rejected rather than partially processed.

ParametersJSON Schema
NameRequiredDescriptionDefault
mintsYes1-25 Solana token mint addresses

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does a good job: it discloses batching limits, per-call billing equivalence, no discount, and all-or-nothing rejection. It still relies on referencing check_token_risk for the actual risk-check behavior and does not describe the return format, but the critical behavioral traits are disclosed.

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?

Two sentences contain all necessary information, with the core batching purpose front-loaded. The billing and quota behavior are compactly stated with no filler.

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 one-parameter tool with no output schema, the description covers invocation scope, limits, billing, and failure behavior. It is slightly incomplete only in that it defers the actual risk-check semantics to check_token_risk rather than describing them, but this is acceptable for a thin batch wrapper.

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 schema already provides 100% coverage of the sole parameter with a clear description ('1-25 Solana token mint addresses'). The description adds billing and batching semantics but no new syntactic or format details beyond what the schema provides, so the baseline 3 applies.

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 states the tool is the same as check_token_risk but for up to 25 mints in one call, which clearly identifies the verb (check risk), the resource (Solana tokens), and the batch scope. It differentiates from the sibling check_token_risk by batching and from get_token_risk_history by being a risk check rather than history.

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?

It explicitly says the tool should be used instead of check_token_risk when you have up to 25 mints in one call, and it explains the economics (N mints charged as N calls, no bulk discount) and the all-or-nothing quota behavior. These are concrete conditions for choosing this tool over the single-mint sibling.

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

get_token_risk_historyGet historical risk trend for a Solana tokenAInspect

Returns hourly historical data points (safety_score, insider_cluster_count, holder_count, price, liquidity, volume) for a mint over the last N days (max 90). Free and unmetered — no quota, works with or without an API key — pure read from stored history, no live upstream call.

ParametersJSON Schema
NameRequiredDescriptionDefault
daysNoHow many days of history to return (default 30, max 90)
mintYesThe Solana token mint address

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations at all, the description carries the full behavioral burden and does so excellently. It discloses that the tool is read-only ('pure read from stored history'), has no quota ('Free and unmetered'), needs no API key ('works with or without an API key'), and makes no live upstream call. This is rich, useful behavioral context.

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 three tight sentences with no filler. It front-loads the core function and returned fields, then adds the behavioral caveats. Every clause earns its place and the structure is easy to scan.

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?

For a simple two-parameter read-only tool with no annotations and no output schema, the description is remarkably complete. It covers purpose, output shape (listed fields), time window, quota, auth, and upstream behavior. An agent has everything needed to call it 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 both mint and days are already documented. The description reinforces the max-90-day limit and adds hourly granularity, but it does not add meaningful new parameter-level meaning beyond the schema. 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?

The description uses a specific verb ('Returns') with a clear resource ('historical risk trend for a Solana token') and enumerates the exact data fields returned. The word 'historical' plus the time-window framing distinguishes it from siblings check_token_risk and check_token_risk_batch, which sound current-state oriented.

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 clearly implies when to use it: when a historical trend over multiple days is needed, not a current snapshot. It does not explicitly name the sibling alternatives or state when not to use the tool, but the context is unambiguous enough for an agent to make the right choice.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.0
    • First observedcheck_token_risk
    • First observedcheck_token_risk_batch
    • First observedget_token_risk_history
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TDQS

A4.5/5.0

Scored across 3 tools

Disambiguation5/5

The three tools are clearly distinct: check_token_risk covers a single token snapshot, check_token_risk_batch explicitly handles multiple mints, and get_token_risk_history addresses historical data. There is no meaningful overlap or ambiguity between current risk checking, batch risk checking, and historical trends.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: check_token_risk, check_token_risk_batch, and get_token_risk_history. The shared token_risk core with clear qualifiers makes the toolset predictable and easy to navigate.

Tool Count5/5

Three tools is appropriately scoped for a focused insider-cluster risk API: single-token checks, batch checks, and historical data. Each tool serves a distinct and necessary purpose without redundancy or bloat.

Completeness5/5

The toolset covers the core domain completely: immediate single-token risk assessment, scaled batch assessment, and historical trend analysis. Since the API is read-only risk intelligence, no create/update/delete operations are expected, and no significant capability gap is apparent.

Maintenance

ActivityActive
ResponsivenessSlow

Resources

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