Composite XLM market sentiment in [-1, 1] (positive = bullish), with per-component breakdown, live XLM price, and bid/ask spread. Output is a JSON string with keys: sentiment, sentiment_source, components, price_usd, mid, spread_pct, ts.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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 are provided, so the description carries the full burden. It is transparent about the output format, the normalized sentiment range, the interpretation of positive values, and the included components (price, spread, timestamp). It does not mention failure modes or latency, but for a zero-parameter read-style tool this is sufficient.
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?
One dense sentence front-loads the core result and then lists the exact output keys. Every clause adds useful information and there is no filler or redundancy.
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 no parameters, no siblings, and an output schema already present, the description fully equips an agent to call and parse the result. It defines the sentiment scale and explicitly enumerates the JSON keys, making the tool 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?
The tool has zero parameters, so the schema already covers everything. The description adds no parameter details, but none are needed; the baseline for zero-parameter tools applies.
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 specific resource (XLM market sentiment) and a precise scope: composite sentiment in [-1, 1] with direction semantics, per-component breakdown, live price, and spread. It clearly identifies what the tool returns and leaves no ambiguity about its function.
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?
There are no sibling tools, so no alternative routing is needed. The description gives clear context for when this tool is appropriate: when an agent needs a composite, quantified sentiment signal for XLM plus related market data. It stops short of explicit when-to-use/when-not-to-use phrasing, but the use case is obvious from the description.
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 is only one tool, so there is no possibility of confusion between tools. The single tool's purpose is clearly defined and self-contained.
The tool name follows a clear verb_noun pattern (get_xlm_sentiment), which is consistent and descriptive. With only one tool, there are no conflicting conventions.
A single tool feels thin even for a focused sentiment server. The count is borderline acceptable, but it leaves little room for complementary operations such as historical sentiment or multi-asset support.
The tool provides a composite sentiment score, component breakdown, live price, and spread data, covering the core domain well. Minor gaps exist, such as no historical or configurable sentiment analysis, but the current surface is functional for its stated purpose.