alphapipeline

security.contract_health_audit

Read-only

Use this tool to check whether a token's liquidity pool is locked, burned, or freely held by a single wallet before trusting its liquidity - a rug-pull signal that security.token_risk does not cover. Reuses the same GoPlus data as token_risk (no extra upstream call) and rolls it up into a liquidity_health category (LOCKED/PARTIALLY_LOCKED/UNLOCKED/NO_LP_DATA). Does not include any qualitative 'suspicious transaction' judgment - only GoPlus's own lock/burn numbers. Do not use for honeypot or tax checks (use security.token_risk) or market price data. Paid in USDC on Base.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`chain_id`	Yes	EVM chain id, e.g. 8453 for Base.
`contract_address`	Yes	Token contract address (0x...).

Output Schema

TableJSON Schema

Name	Required	Description
`notice`	No
`chain_id`	Yes
`risk_flags`	Yes
`token_name`	No
`data_source`	Yes
`generated_at`	Yes
`token_symbol`	No
`lp_burned_pct`	No
`lp_locked_pct`	No
`lp_holder_count`	No
`lp_total_supply`	No
`contract_address`	Yes
`liquidity_health`	Yes
`top_unlocked_holder_pct`	No
`top5_unlocked_holder_pct`	No	Sum of the top 5 non-locked/non-burned LP holders - catches concentration spread across several wallets that top_unlocked_holder_pct (single largest holder only) would miss.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 9, 2026

addedOutput schema / properties / top5_unlocked_holder_pct

Added value: +{
+  "description": "Sum of the top 5 non-locked/non-burned LP holders - catches concentration spread across several wallets that top_unlocked_holder_pct (single largest holder only) would miss.",
+  "type": [
+    "number",
+    "null"
+  ]
+}

First observedSep 8, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With annotations only covering read-only safety, the description adds substantial context: the GoPlus data source and that it costs no extra upstream call, the exact liquidity_health enum values returned, the boundary that it excludes qualitative 'suspicious transaction' judgment, and the USDC-on-Base payment model.

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?

Front-loaded with the use case, then data source, output categories, scope limits, and cost. Every sentence carries distinct information and none is filler.

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?

An output schema exists, so return values need not be detailed, yet the description still surfaces the category enum. Usage conditions, scope boundaries, data provenance, and pricing are all present, leaving nothing an agent needs 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% for both parameters (chain_id, contract_address), so the schema already documents them fully. The description adds no syntax or format meaning beyond the schema, making the baseline 3 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 (check) and resource (a token's liquidity pool lock/burn/free-hold status) with clear scope, and explicitly distinguishes itself from the sibling security.token_risk by naming the rug-pull signal token_risk does not cover.

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?

Gives an explicit when ('before trusting its liquidity'), explicit exclusions ('Do not use for honeypot or tax checks (use security.token_risk) or market price data'), and names the alternative tool. Nothing is left to inference.

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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