Domain-targeted SUBSTRATE intelligence briefing. Narrower focus, higher signal. Top lifeforms + breakthroughs in one domain. Domains: ai, climate, biology, energy, economics, materials. $0.05. Requires API key.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Target domain for the intelligence briefing. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and determinism. The description adds valuable context by disclosing '$0.05' cost and 'Requires API key' authentication needs, which are not in annotations. It also describes the output focus ('Top lifeforms + breakthroughs'), but does not delve into pagination or output format, which is acceptable given the read-only nature.
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 and front-loaded: the first sentence establishes purpose, followed by scope, output, domains, cost, and auth. Every sentence earns its place, with no fluff or redundancy. It efficiently packs essential information into a short text.
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 the low complexity (one parameter, no output schema, read-only), the description is highly complete. It covers purpose, valid domain values, cost, authentication, and a preview of the output content ('Top lifeforms + breakthroughs'). Annotations fill in safety (read-only, idempotent), leaving no critical gaps for an agent to invoke the tool correctly.
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%: the parameter 'domain' is fully described with an enum and definition. The description's repetition of 'Domains: ai, climate, biology, energy, economics, materials' adds no new meaning beyond the schema. Since the schema carries the semantic burden, the description meets the baseline without adding extra parameter guidance.
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 function: 'Domain-targeted SUBSTRATE intelligence briefing' with 'Top lifeforms + breakthroughs in one domain.' It explicitly lists the supported domains, distinguishing it from broader digests. The verb 'digest' and resource 'SUBSTRATE' are specific, and the scope is narrowed by 'Domain-targeted.'
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 phrase 'Narrower focus, higher signal' implies a comparison to broader alternatives like the daily digest, suggesting when this tool is appropriate. It provides the domain list as valid inputs. However, it does not explicitly name sibling alternatives or state when not to use this tool, so it stops short of full usage guidance.
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.
Many tools have overlapping purposes, such as multiple search tools (web_search, deep_search, data_retrieval) and multiple communication tools (agent_ping, agent_telegram, swarm_broadcast). The descriptions help differentiate, but the boundaries are not always clear.
All tools consistently use the 'x711_' prefix and lowercase_with_underscores format. Submodules like agent, hive, and tx follow predictable patterns. Minor deviations (e.g., x711_ask_clerk) are rare and still descriptive.
With 47 tools, the server is excessively large for a typical MCP service. While it aims to be a comprehensive platform, the high count makes navigation and selection cumbersome for an agent.
The tool set covers a wide range of agent needs: web access, memory, communication, on-chain transactions, code execution, and more. Minor gaps exist (e.g., no agent deletion tool), but overall it is remarkably complete for the stated purpose of an agent platform.