Launch radar: early-stage Robinhood Chain candidates discovered from X chatter + onchain scans, each with the three-output assessment (riskGate pass|watch|fail, opportunity 0-100 or blocked, data confidence high|medium|low), safety score, liquidity and paid-promotion flag. A high score means 'nothing obviously wrong observed onchain', never 'guaranteed legit'.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max rows (default 20) | |
| status | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavioral traits: three-output assessment, safety score, liquidity, paid-promotion flag, and a critical caveat that a high score does not guarantee legitimacy. This adds significant transparency beyond structured fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with key information. The first sentence covers purpose and output, the second adds an important caveat. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains the output structure and limitations. It covers the main aspects but lacks details on pagination or interpretation of the three-output gates. Adequate for a radar tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (limit has description; status has enum but no description). The description explains the output assessment but does not clarify the meaning of the status filter values (watching, clean, flagged) in input context. It adds some value but is not fully compensatory.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states what the tool does: lists early-stage Robinhood Chain candidates from X chatter and onchain scans, with a three-output assessment. It distinguishes itself from siblings like check_token_risk and get_trending_tokens by focusing on early-stage candidates with specific evaluation criteria.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for discovering early-stage candidates, and the sibling tools provide alternatives for detailed risk or trending. However, it does not explicitly state when not to use or provide direct comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
