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lordbasilaiassistant-sudo

base-price-oracle-mcp

get_liquidity_depth

Analyze token liquidity depth by checking pool reserves across all DEXes on Base blockchain to assess market stability and trading conditions.

Instructions

Check pool reserves and liquidity depth for a token across all DEXes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
token_addressYesToken contract address on Base

Implementation Reference

  • Implementation of the get_liquidity_depth MCP tool, which queries DEX pools for token reserves and liquidity data.
    server.tool(
      "get_liquidity_depth",
      "Check pool reserves and liquidity depth for a token across all DEXes",
      {
        token_address: z.string().describe("Token contract address on Base"),
      },
      async ({ token_address }) => {
        try {
          const quoteAddress = WETH;
          const [tokenDecimals, quoteDecimals, tokenSymbol] = await Promise.all([
            getTokenDecimals(token_address),
            getTokenDecimals(quoteAddress),
            getTokenSymbol(token_address),
          ]);
    
          const pools = await findAllPools(token_address, quoteAddress);
          if (pools.length === 0) {
            return { content: [{ type: "text" as const, text: `No DEX pools found for ${token_address} on Base.` }] };
          }
    
          const poolData = pools.map((pool) => {
            const price = calculatePrice(pool, tokenDecimals, quoteDecimals);
    
            if (pool.sqrtPriceX96 !== undefined) {
              // V3 pool — report liquidity
              return {
                dex: pool.dex,
                pool: pool.address,
                type: "concentrated",
                liquidity: pool.liquidity?.toString() ?? "0",
                price: formatEth(price),
              };
            }
    
            // V2/Aerodrome — report reserves
            const tokenReserve = pool.tokenIsToken0
              ? Number(ethers.formatUnits(pool.reserve0, tokenDecimals))
              : Number(ethers.formatUnits(pool.reserve1, tokenDecimals));
            const ethReserve = pool.tokenIsToken0
              ? Number(ethers.formatUnits(pool.reserve1, quoteDecimals))
              : Number(ethers.formatUnits(pool.reserve0, quoteDecimals));
    
            return {
              dex: pool.dex,
              pool: pool.address,
              type: "constant-product",
              tokenReserve: tokenReserve.toFixed(4),
              ethReserve: formatEth(ethReserve),
              totalValueETH: formatEth(ethReserve * 2),
              price: formatEth(price),
            };
          });
    
          return {
            content: [{
              type: "text" as const,
              text: JSON.stringify({
                token: token_address,
                symbol: tokenSymbol,
                poolCount: pools.length,
                pools: poolData,
              }, null, 2),
            }],
          };
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions checking across 'all DEXes', which adds some context about scope, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or what the output format might be. This leaves significant gaps for a tool that likely queries external data.

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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the complexity of querying liquidity across multiple DEXes, the lack of annotations and output schema means the description should do more to explain behavioral traits, return values, or limitations. It currently provides only a high-level purpose, which is insufficient for an agent to fully understand how to use this tool effectively.

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 has 100% description coverage, with the single parameter 'token_address' documented as 'Token contract address on Base'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating value.

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 clearly states the action ('Check') and the target resources ('pool reserves and liquidity depth for a token across all DEXes'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_market_summary' or 'get_token_price', which might also involve token data, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools such as 'compare_prices' or 'get_price_impact', leaving the agent without context for selection.

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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