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Glama

detect_insider_clusters

Analyze top token holders to detect Sybil bundling, shared gas funding, and dev cabal rug-pull risks.

Instructions

Performs deep Sybil bundling and dev cabal graph analysis on top token holders to detect shared gas funding sources and rug pull risks.

Input Schema

NameRequiredDescriptionDefault
networkYesBlockchain network (e.g. base, solana)
paymentProofNoOptional Base USDC txHash (0x...) or x402 payment proof for unlimited calls
tokenAddressYesContract address of the token (0x... or Solana base58)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It implies a heavy computation ('deep ... graph analysis') but never states cost, latency, rate limits, or that the operation is read-only, and it says nothing about the payment/credit model despite a paymentProof parameter existing for 'unlimited calls'.

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?

A single front-loaded sentence with no filler; the action and detection targets come first. The jargon density ('Sybil bundling', 'dev cabal') is efficient for a domain-aware reader but borders on opaque.

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

Completeness3/5

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

For a three-parameter tool with a fully described schema but no output schema and no annotations, the description identifies the detection targets (shared gas funding, rug pull risk) but omits return structure, cost/payment expectations, and latency. Adequate to call correctly, not adequate to set expectations.

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 network, tokenAddress, and paymentProof are already documented in the schema. The description only adds the scoping hint that analysis is limited to 'top token holders' rather than all holders, which is marginally useful but not compensating detail.

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 names a specific analysis (Sybil bundling and dev cabal graph analysis) on a specific resource (top token holders) and states the goal (detecting shared gas funding sources and rug pull risks). It is clear what the tool does, but it does not differentiate itself from siblings such as get_token_whale_activity or get_smart_money_radar, which overlap in the holder-analysis space.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternatives. An agent must infer from the phrase 'rug pull risks' that this is a token-risk-due-diligence tool rather than a general holder or smart-money lookup, leaving the choice among four sibling tools to guesswork.

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