Check this operator's tax balance at the Authority.
Returns the sats available for certifying patron credit purchases. When this balance reaches zero, patron top-ups cannot be certified and the operator must call purchase_credits on the Authority.
This is the operator's own funding — not a patron balance. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
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No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that this is a check (read-only) that returns 'the sats available' and is 'Free.' It also explains the operational consequence of a zero balance and directs the operator to purchase_credits. This is solid behavioral transparency, though it doesn't describe the output structure (output schema exists).
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 four sentences, each adding meaningful context: the core purpose, what the balance represents, the consequence of zero, and a key distinction from patron balances. It is front-loaded and free of filler, making it easy for an agent to parse.
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 with no parameters, and the description covers what it returns (sats available), what it is for (certifying patron top-ups), what to do when it's exhausted (purchase_credits), and a critical distinction (operator vs. patron balance). This fully addresses the agent's needs for invocation and decision-making.
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 takes zero parameters, and the schema has no properties. The description adds meaning by explaining that the 'balance' refers to operator funding for certifying patron purchases, not a patron balance. Since there are no parameters to document, the description adequately conveys the semantic of what is being checked.
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 opens with a specific verb+resource ('Check this operator's tax balance at the Authority') and further clarifies the exact meaning of the balance (sats available for certifying patron credit purchases). It explicitly states it is the operator's own funding, not a patron balance, which distinguishes it from siblings like brain_check_balance. This is a clear, unambiguous purpose.
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 context for when this tool is relevant: when the balance reaches zero, patron top-ups cannot be certified and the operator must call purchase_credits. It also differentiates this from a patron balance check, implying when to prefer this tool over alternatives. However, it does not explicitly name sibling tools or provide a 'when not to use' statement.
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.
Multiple tools have overlapping purposes. For example, `brain_request_credential_channel` and `brain_request_patron_credentials` serve similar roles, and `brain_receive_credentials`, `brain_receive_npub_proof`, and `brain_receive_patron_credentials` all handle receiving data from a courier flow. While descriptions help, the sheer number of tools (83) with similar-sounding purposes (check_ vs get_ vs request_ vs receive_ prefixes) makes it hard to quickly distinguish which tool to use.
The tools mostly follow a `brain_verb_noun` pattern (e.g., `brain_create_thought`, `brain_delete_link`), which provides some consistency. However, there are inconsistencies with prefixes like `brain_oracle_` (e.g., `brain_oracle_about`, `brain_oracle_how_to_join`) which are more like static pages than actions. Additionally, 'check' and 'get' seem interchangeable (e.g., `brain_check_balance` vs `brain_get_thought`), and 'list' is used alongside 'get' in a way that sometimes means the same thing (e.g., `brain_list_brains` vs `brain_get_brain`).
83 tools is an extremely large and unwieldy surface area. While the server aims to be a comprehensive 'operating system' for a specific ecosystem (DPYC/Nostr), this many tools will lead to agent confusion and high latency. Tools like `brain_oracle_about`, `brain_oracle_how_to_join`, and `brain_oracle_network_advisory` could easily be combined into a single tool or served as function parameters.
For its stated domain (managing a 'brain' with credits, payments, and Nostr integration), the tool set is remarkably complete. It covers CRUD operations, payment flows (purchase, check, restore), coupon management, credential handling, and even notarization. Minor gaps are hard to identify, though some flows feel overly complex (e.g., the multiple `request_`/`receive_` patterns could arguably be simplified). The high number of tools is a result of this extreme specialization.