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Glama

0xrhXBT — Robinhood Chain Intelligence

chain_composition

What Robinhood Chain is made of: on-chain value and holders by asset class (stock / stable / memecoin / other), from our own token registry joined against each token's freshest market observation, plus the settled daily RWA-share series (tokenized-equity value over everything priced) — the measured answer to 'equities chain or memecoin chain'. Precomputed on the agent every 5 minutes; the board carries computedAt/validUntil and is withheld with a dataNote past its ceiling. A class with no priced token is null with pricedTokens saying how much of the registry backs each number — never a fabricated zero. The daily series has no freshness gate (history does not age into being wrong); a missing day is a day the precompute never ran.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description fully discloses key behavioral traits: precomputed every 5 minutes, has computedAt/validUntil fields, withheld with a dataNote past its ceiling, null handling for asset classes with no priced token (never a fabricated zero, with pricedTokens indicating backing), and the daily series having no freshness gate with missing days meaning the precompute never ran. This is exceptionally transparent for a zero-parameter 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 long but information-dense, with each sentence adding meaningful detail: purpose, data sources, refresh cadence, null policy, and freshness semantics. It is front-loaded with the core purpose before diving into technical specifics. While it could be tightened, no sentence is wasted or redundant.

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

Completeness5/5

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

For a zero-parameter tool with no output schema, the description covers all necessary context: what it computes, how often it refreshes, validity windows, edge cases (null classes, missing daily series), and the meaning of the data. An agent would have everything it needs to understand what the tool returns and how to interpret it.

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

Parameters4/5

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

The tool has zero parameters, and the schema is empty with 100% coverage. The baseline for 0-parameter tools is 4, and the description adds no parameter semantics because there are none to describe. The description still contributes by clarifying what data the tool returns, which is beyond parameter concerns.

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 clearly states the tool's purpose: reporting chain composition by asset class (stock/stable/memecoin/other) with on-chain value and holders, plus the daily RWA-share series. It frames it as the 'measured answer to equities chain or memecoin chain', which is specific and distinct from the sibling tools like chain_economics or get_chain_stats. The verb is implied (provides/reports) but the resource and scope are unambiguous.

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 implies usage for understanding the chain's composition and answering whether it's an equities or memecoin chain. It gives clear context on what the tool delivers (the measured answer), but does not explicitly contrast with alternatives or state when to avoid it. Sibling differentiation is not explicit, but the unique output makes the use case obvious.

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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