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loophole-tape

recent_graduations

$0.02: recent migrations labelled true-graduation (curve witnessed >= 84 SOL) vs low/fake close pools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
window_sNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
Behavior4/5

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

With no annotations provided, the description takes on the full burden and does disclose the cost ($0.02), the classification scheme (true-graduation vs low/fake), and the exact threshold used (curve witnessed >= 84 SOL). It does not cover ordering, pagination, or rate limits, but for a read-only list tool the key 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence with no filler; the threshold and classification are packed efficiently. The '$0.02' prefix is off-topic from purpose but still useful and does not bloat the definition.

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?

There is no output schema, no annotations, and the description does not explain what fields are returned, what 'curve' or 'close pools' mean, or how limit/window_s shape results. An agent can call it with defaults, but cannot confidently reason about output or parameter effects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not mention 'limit' or 'window_s' at all. The parameter names are mildly self-explanatory, but no meaning or constraints are added to compensate for the missing schema descriptions.

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?

Although it lacks an explicit verb, the description identifies the resource ('migrations') and the distinguishing output ('labelled true-graduation ... vs low/fake close pools') with a concrete threshold (>= 84 SOL). This separates it from siblings recent_launches and recent_rugs by domain and classification.

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

Usage Guidelines3/5

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

No explicit when/when-not guidance or alternative tools are named. The phrase 'recent migrations labelled true-graduation ... vs low/fake close pools' implies the tool is appropriate when the agent needs graduation/migration classifications rather than launches or rugs, but this 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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TDQS

A3.7/5.0
Disambiguation5/5

Each tool serves a distinct analysis layer: market regime, launch lists, graduation/rug event lists, per-mint risk cards, creator reputation, and wallet profiling. Even the demo tool (sample_mint) is clearly framed as a preview of mint_risk_card rather than a competing endpoint.

Naming Consistency5/5

All tool names follow the same snake_case, descriptive noun-phrase pattern (creator_reputation, mint_risk_card, recent_launches). There is no mixing of verb styles or camelCase, making the toolset predictable for an agent.

Tool Count5/5

Eight tools is well within the ideal range and each tool covers a meaningful slice of the pump.fun risk-analysis domain. No tool feels redundant or purely cosmetic.

Completeness5/5

The set covers the full workflow: market context, recent launches, graduation and rug outcomes, per-mint deep dives, creator history, and wallet classification. A user can trace a launch from listing through graduation or rug and then pull entity-level context without hitting dead ends.