get_analyst_accuracyScored track record of public crypto commentators and influencers whose calls Einstein has graded against what actually happened. Ranked by measured hit-rate, not follower count. Reports how many calls are graded versus still awaiting their horizon, and says plainly when the record is too thin to rank.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return (1-25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond that: it reports how many calls are graded versus pending, and how thin-data cases are handled by refusing to rank. This helps an agent understand edge-case output without contradicting annotations.
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?
Three tight sentences, each carrying distinct information: the resource, the ranking metric, and the output/reporting behavior. No fluff or repetition of schema details, and the key differentiator ('not follower count') is front-loaded near the beginning.
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?
For a simple, read-only tool with one optional parameter and no output schema, the description gives sufficient operational context: what is ranked, how ranking is determined, and how incomplete records are handled. It could go further by specifying exact output fields, but the description is not incomplete enough to hamper correct invokation.
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 description coverage is 100% for the only parameter (limit), so the schema already contains all necessary semantics. The description adds nothing about the limit parameter, which is acceptable because the schema fully documents it.
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 states a clear verb and resource: a scored track record of crypto commentators/influencers graded against actual outcomes. It distinguishes itself from popularity-based tools by explicitly saying 'Ranked by measured hit-rate, not follower count,' which differentiates it from siblings like get_social_hype or get_trade_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?
The intended use is implied: this is the tool for influencer accuracy and graded calls. However, it does not explicitly state when to use it over siblings like get_forecast_accuracy or get_trade_signals, nor does it mention any exclusions.
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.
With 40 tools, many share overlapping domains: get_smart_money_flow vs get_smart_money_inflow, scan_launchpads vs get_launchpad_radar, track_whales vs get_hyperliquid_whales, and check_token_safety vs analyze_token_security. The detailed descriptions help, but the boundaries are not always clear, making misselection likely.
The tool names employ a wide variety of verbs (get_, analyze_, scan_, track_, find_, generate_, recommend_, run_, list_, ask_, assess_, detect_) with no consistent pattern. While all use snake_case, the inconsistent verb choices and occasional deviations like forecast_chart prevent predictability.
40 tools is well above the typical 3-15 well-scoped range and exceeds the 25+ threshold for 'too many'. While the broad 'crypto intelligence' purpose justifies some breadth, the sheer number makes the surface unwieldy and suggests a lack of focused scoping.
The tool set covers a wide range of crypto intelligence domains: market analysis, forecasting, whale tracking, yield/arbitrage, security checks, prediction markets, backtesting, and even content generation. Missing operations are minor (e.g., no direct portfolio management), but core analysis and data retrieval workflows are well represented.