Live Bitcoin mempool fee rates from mempool.space.
Returns recommended fees in sat/vB: fastest, half-hour, economy, minimum. Fees above 50 = congestion, above 100 = extreme. Critical for X-Ray sensor verification.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds useful behavioral context by explaining fee thresholds (above 50 = congestion, above 100 = extreme) and noting the live data source, which goes beyond a simple read-only description.
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 three concise sentences, front-loaded with the main purpose, and every sentence adds value: source, return values, threshold interpretation, and use-case context.
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, parameter-free read tool with an output schema, the description covers the essential elements: source, output format, interpretation, and use case. It lacks details on data freshness or error responses, but these are minor gaps for this tool's simplicity.
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 baseline is 4. The description appropriately focuses on output semantics rather than parameter details, as no parameters exist to explain.
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 returns live Bitcoin mempool fee rates and specifies the exact outputs (fastest, half-hour, economy, minimum in sat/vB), which distinguishes it from siblings like get_mempool_stats and get_fee_histogram.
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 identifies a specific use case ('Critical for X-Ray sensor verification') but does not explicitly compare to alternatives or state when not to use this tool. Implied usage is available, but no exclusion or direct sibling comparison is provided.
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.
Tools like get_convergence, get_directional_bias, and get_dashboard are related but clearly scoped: convergence checks sensor agreement, directional_bias gives the trade call, dashboard bundles everything. Mempool fees vs stats are distinct (rates vs pending tx). Some overlap exists between convergence/regime_current, but descriptions disambiguate well.
All tools follow a consistent get_verb_noun pattern (get_block_tip, get_funding_divergence, get_system_health). The only exception is query_db, which uses 'query' instead of 'get', but it still follows the verb_noun structure and same snake_case style. No mixed conventions.
15 tools is at the high end of the ideal range, but each serves a distinct function in a complex domain: sensor convergence, regime, funding, gamma, mempool, system health, audit. The Pro/free tier adds some apparent duplication (get_convergence vs get_directional_bias), but they address different questions.
The tool set covers the full workflow: convergence check, directional call, regime context, specialized indicators (funding, gamma, stablecoin flows, fee histogram), mempool data, system health, audit trail, and a queryable database. No obvious dead ends; public signal history and counters support verification.