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fletch-now

fletch-mcp

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by fletch-now

List registry assets

list_assets

Retrieve verified assets on Robinhood Chain by symbol or name, with contract address, decimals, trust, and live state. Filter by tickers; add fields like concentration to analyze float.

Instructions

Every verified asset on Robinhood Chain (chain 4663): Stock Tokens, bridged coins, USDG, WETH, each with its contract address, decimals, trust and live state (multiplier, pauses, Chainlink price, holders, second-source agreement). Filter with q (symbol or name substring) or symbols (comma-separated exact tickers). fields adds lookalikes, corporateActions, multiplierHistory, feedRounds or concentration per asset, for up to 50 assets — concentration answers which Stock Tokens have the least float in one request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoSymbol or name substring
fieldsNoComma-separated extras: lookalikes,corporateActions,multiplierHistory,feedRounds,concentration
symbolsNoComma-separated tickers, e.g. TSLA,AAPL

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full disclosure burden, and it does well: it discloses the scope (verified assets only), per-asset return contents (multiplier, pauses, Chainlink price, holders, second-source agreement), filter semantics, and the 50-asset enrichment cap. It is silent on pagination, default result limits, and result ordering, which keeps it from a 5.

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 one dense paragraph but is logically ordered: scope → returned data → filtering → enrichment. The core listing purpose is front-loaded and every clause carries information. It borders on long, but there is no padding given the number of behaviors it needs to convey.

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

Completeness4/5

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

For a tool with no output schema and no annotations, the description effectively documents the return payload inline (address, decimals, trust, live state) along with parameter behaviors and a result cap. The main gaps are the unresolved sibling overlap (fields values mirror dedicated sibling tools, with no routing guidance) and the absence of pagination/default-limit behavior.

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 the schema already documents q, fields, and symbols, giving the baseline of 3. The description adds context for fields (per-asset semantics, the 50-asset cap, and the concentration use case) but largely restates the filter behavior already present in the schema, so it does not meaningfully exceed the baseline.

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?

The description states a specific verb and resource ('Every verified asset on Robinhood Chain (chain 4663)') and enumerates exactly what is included: Stock Tokens, bridged coins, USDG, WETH, with contract address, decimals, trust, and live state. It clearly differentiates from the sibling get_asset by being the plural/list variant and distinguishes itself from lookalikes, corporate_actions, feed_rounds, and holders by offering those as optional fields within the list response.

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

Usage Guidelines4/5

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

The description explains the two filtering modes (q substring vs. symbols exact tickers) and gives an explicit use case for the enrichment path ('concentration answers which Stock Tokens have the least float in one request'). However, it never names sibling tools as alternatives or states when NOT to use this tool — an agent must infer the boundary against the dedicated lookalikes, corporate_actions, feed_rounds, and holders endpoints.

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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