FortFi Treasury MCP

screen_tokens

Batch cheap-screen tokens across requested chains. Defaults to verified assets, $5M market cap, $50K daily volume, top 30-day performers plus volume leaders, stablecoin removal, and cross-chain deduplication. Returns no proposal.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "chains": "<chains>"
    },
    "name": "screen_tokens"
  }
}

Input Schema

Name	Required	Description
`mode`	No
`sorts`	No	Defaults to gainers_30d + volume_1d. Add gainers_1d, gainers_90d, gainers_365d, or market_cap when requested.
`chains`	Yes
`deepOptIn`	No	Required when mode=deep
`verifiedOnly`	No	Default true
`candidateLimit`	No	Override merged candidate cap, max 100
`minVolume1dUsd`	No	Default 50000
`minMarketCapUsd`	No	Default 5000000
`includeUnverified`	No	Default false; unverified results are labeled and need execution opt-in

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 3, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses default filtering behavior (verified assets, market cap/volume thresholds, stablecoin removal, deduplication) and clarifies that no proposal is returned. However, it does not describe the return shape, permissions, or whether the operation is strictly read-only.

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 compact and front-loaded, with three sentences that each carry useful information. It avoids repetition and stays focused on screening behavior and defaults.

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 9-parameter tool with no annotations and no output schema, the description covers the core screening behavior and defaults but leaves significant gaps around return format, pagination or output shape, and explicit sibling-tool selection. It is adequate but not complete.

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 is 78%, so most parameters are already documented. The description adds some meaning by restating key defaults and mentioning stablecoin removal and cross-chain deduplication, but it does not explain the mode enum, candidateLimit, or deepOptIn behavior beyond what the schema already says.

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 states a specific verb and resource: 'Batch cheap-screen tokens across requested chains.' It is clear that the tool screens tokens rather than ranking or discovering them, though it does not explicitly distinguish itself from sibling tools like rank_tokens or discover_tokens.

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 gives useful defaults but provides no explicit when-to-use guidance, no exclusions, and no comparison to alternatives such as discover_tokens or rank_tokens. An agent must infer when screening is appropriate.

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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