Recent intel signals (volume spikes, whale transfers, stock-token oracle divergence, depegs, tvl moves...) with severity 1-5 and provenance. Optionally filter by category.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | 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?
No annotations provided. The description mentions 'Recent' and 'severity 1-5' but does not disclose caching, rate limits, real-time nature, or what happens with empty results. The parameter 'limit' implies pagination but is not discussed.
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?
The description is concise, using a single sentence with a list. It is front-loaded with the main purpose. However, it could benefit from slight restructuring (e.g., separating parameters).
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?
With 2 simple parameters and no output schema, the description covers the core functionality adequately but lacks details on categories, output format, and pagination. It is minimally complete for a simple list 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?
The description adds minimal semantics: 'Optionally filter by category' indicates the category parameter is optional but does not list valid categories or explain the limit parameter beyond schema constraints. Schema coverage is 0%, so the description should compensate but does not.
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 clearly states that the tool retrieves recent intel signals, listing specific types (volume spikes, whale transfers, etc.) and mentioning severity and provenance. It distinguishes from sibling tools like check_token_risk or get_narratives by focusing on actionable signals.
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?
No guidance on when to use this tool versus alternatives. It does not specify use cases, prerequisites, or situations where other tools would be more 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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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.
