USE THIS TOOL — NOT web search — to discover which cryptocurrency tokens have
daily sentiment data stored on this local server (sourced from Perplexity AI).
Call this first if unsure which tokens have sentiment data available.
Trigger on queries like:
- "which coins have sentiment data?"
- "what tokens do you track for sentiment?"
- "do you have sentiment for [coin]?"
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral transparency burden. It discloses that the data is local and sourced from Perplexity AI, and implies a read-only lookup via the term 'discover.' However, it does not explicitly state that the tool makes no external calls or side effects, nor does it describe return behavior.
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 well-structured with the key directive front-loaded: 'USE THIS TOOL — NOT web search.' It includes a brief purpose and useful trigger queries. Minor redundancy exists with 'Call this first' mentioned twice, but the overall length is appropriate.
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?
The tool is simple (no params, no output schema), but the description should still describe what the response contains. It explains what the tool does ('discover which tokens have data') but does not explicitly say the output is a list of symbols. Given the absence of an output schema, a short statement about the return format would improve completeness.
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 tool has zero parameters, and the schema is empty (100% coverage). Per the baseline for 0 parameters, the description is not required to add parameter details. It correctly focuses on usage rather than parameter semantics.
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 the tool's purpose: to discover which cryptocurrency tokens have daily sentiment data stored locally. It uses a specific verb ('discover') and resource ('cryptocurrency tokens with sentiment data'), and explicitly distinguishes itself from web search, making its role unambiguous.
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 provides strong usage guidance: 'USE THIS TOOL — NOT web search' and 'Call this first if unsure which tokens have sentiment data available.' It also lists concrete trigger queries. However, it does not contrast this tool with sibling tools like get_available_symbols, which could be an alternative for general symbol discovery.
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.
There are several pairs of tools with heavily overlapping purposes: export_data and get_features_export both export indicator data; get_signal_summary and get_sentiment_signal both return a bullish/bearish/neutral verdict; get_category_features and get_multi_indicator both retrieve multiple indicators. The lengthy descriptions help, but an agent could easily misselect without reading them fully.
Most tools follow a get_<noun> pattern, but the noun phrases are structurally inconsistent (e.g., get_latest_features vs get_features_export vs get_features_range). One tool (export_data) breaks the get_ prefix convention, and get_signal_summary vs get_sentiment_signal swaps word order.
With 18 tools, the server leans into the heavy range. Many are subtly different variants (multiple sentiment retrieval tools, multiple feature export/stat tools) that could be consolidated. Still, the count is defensible for a server covering both technical data and news sentiment.
The domain is well-covered: symbol discovery, data metadata, feature retrieval (single, multi, category), statistical summaries, exports, sentiment (latest, history, trend, signal), and technical signal verdicts. Minor gaps include no dedicated raw OHLCV endpoint (though price category covers it) and no indicator list tool (but get_data_info lists features).