[marketsummary] Where our editorial desk's coverage leads or lags the crowd's chatter. desk_ahead = we cover it more than the crowd talks about it; crowd_ahead = the reverse. A cadence comparison nobody else can compute (our digest_selections vs mentions), NOT a buy signal. Free = 2 desk-ahead names; premium = both lists with the gap. Not financial advice.
| Name | Required | Description | Default |
|---|---|---|---|
| payment | No | ||
| session_token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description adds context: it states the computation basis (digest_selections vs mentions), explains output tiers (free vs premium), and includes a 'not financial advice' disclaimer. It doesn't disclose auth requirements or side effects, but for a read-only info tool this is reasonable.
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 compact, with definitions and a caveat. The 'nobody else can compute' phrase is marketing fluff but not harmful. Generally well-structured.
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?
Given no output schema, the description explains the core metric, defines the two possible directions, and specifies output scope by tier. It lacks explicit session/payment parameter guidance but is largely self-contained.
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 has two params with zero description coverage, and the description doesn't mention them by name. However, it indirectly explains the 'payment' parameter's effect via free vs premium output tiers, partially compensating. 'session_token' remains unexplained.
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 explains the tool measures the gap between editorial coverage and crowd chatter, defining desk_ahead and crowd_ahead. It distinguishes from siblings by its unique cadence comparison, though it lacks a direct 'returns' verb.
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?
It implies usage for assessing coverage vs crowd sentiment, and explicitly warns it's NOT a buy signal. However, it doesn't name alternative tools or specify when to use this instead of others.
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.
Each tool targets a distinct metric or action: attention shifts, buzz score, trending, and narrative tracking measure different facets; longs, shorts, and setups use different strategies; market regime, breadth, and summary are clearly separated. No two tools appear to produce the same output.
All tools follow a consistent pattern: a subsystem prefix (marketsummary__, tokensafety__, verify__) followed by an imperative verb_noun in snake_case. Verbs are predictable (get, find, track, ask, check, request, verify) and objects are clear, making the naming highly uniform.
24 tools is above the typical 3-15 range, but the server covers a broad market-intelligence domain with distinct feature areas. The tools are namespaced by subsystem, which helps agents navigate, but the volume still adds selection overhead.
The set covers market analysis, trending, sentiment, trade setups, token safety, and wallet verification for premium access. Minor gaps exist (e.g., no direct price/OHLC tool or token search), but agents can work around them using the provided tools.