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Glama

0xrhXBT — Robinhood Chain Intelligence

get_equity_flows

Per-stock settlement activity — the layer that exists nowhere else: peer-to-peer share transfers with the share that settled while the US market was CLOSED (offHoursPct, the defining statistic of a 24/7 equity chain), underlying shares minted and burned (a direct read on real-world tokenization demand), and DvP prints — real executions that never touched a DEX pool — with their volume-weighted implied price (dvpVwapUsd). Every figure is computed from chain-timestamped logs. Tokens with no settlement in the window are omitted, never zero-filled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNosettlement window (default 24h)

Schema Changelog

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

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and discloses meaningful behavioral traits: data is 'computed from chain-timestamped logs' and 'tokens with no settlement in the window are omitted, never zero-filled'. These details go beyond a simple data summary and help set expectations for data completeness and source, which is valuable for a read-only tool.

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 dense sentence but every clause contributes specific information about the metrics and behavior. It is not as tightly structured as a two-sentence summary, and the opening phrase 'the layer that exists nowhere else' is slightly promotional, but it avoids redundancy and stays focused on content.

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?

Despite having no output schema, the description covers the key return aspects: what metrics are included (offHoursPct, minted/burned shares, dvpVwapUsd), how they are computed, and the omission policy. It implicitly indicates a per-stock breakdown through 'per-stock settlement activity'. For a tool with a single parameter and relatively clear scope, this is sufficiently 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?

The input schema provides 100% coverage with a single parameter, `window`, including an enum and a description ('settlement window (default 24h)'). The tool description does not mention the parameter or add any additional nuance beyond what the schema already states, so the baseline of 3 is appropriate.

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 identifies the resource as 'per-stock settlement activity' and enumerates specific metrics (offHoursPct, minted/burned shares, dvpVwapUsd), making the tool's scope clear. However, it lacks an explicit verb like 'retrieves' or 'lists', instead using a noun phrase, which slightly reduces clarity. It does differentiate from siblings by emphasizing 'the layer that exists nowhere else'.

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?

The description implies when to use this tool by claiming its content 'exists nowhere else', suggesting it is the sole source for these settlement metrics. It does not name alternative tools or provide explicit when-to-use/when-not-to-use guidance, leaving the user to infer usage from the unique data claims.

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

Every tool targets a distinct slice of Robinhood Chain intelligence: token data, premiums, perp markets, stablecoin flows, corporate actions, risk checks, and sentiment. Even overlapping areas (e.g., get_token vs. search_tokens, get_stock_premiums vs. get_stock_multipliers) are clearly separated by purpose. No two tools appear to duplicate each other's core function.

Naming Consistency3/5

The tool names mix conventions: most are verb-led (get_, search_, check_), but several are noun phrases (chain_composition, perps_markets, stablecoin_flows). Within the get_ group the pattern is consistent, but across the full set the mixing of prefixes and bare nouns makes the naming less predictable. Still, each name is descriptive enough to infer its role at a glance.

Tool Count2/5

With 26 tools, the set exceeds the 25-tool threshold for 'too many'. While the domain is broad (covering tokens, perps, stablecoins, corporate actions, flows, and narratives), the sheer number risks over engineering and agent confusion. Some tools could be grouped (e.g., perps_funding and perps_markets might be one, get_token and get_token_candles might share). The scope feels stretched.

Completeness4/5

The surface covers a comprehensive array of Robinhood Chain data: token details, market premiums, perp funding, stablecoin flows, settlement graphs, corporate actions, and risk assessment. Minor gaps exist (e.g., no direct wallet transaction history, no governance queries), but for the stated purpose of 'chain intelligence' the coverage is robust and includes both live and historical reads.

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