List every coupon this operator has minted (newest first).
Each row carries the current times_redeemed counter — the
Studio renders a progress bar from this against total_uses.
RESTRICTED to operator — requires proof.
| Name | Required | Description | Default |
|---|---|---|---|
| dpop_token | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It discloses the newest-first ordering, that each row includes the 'times_redeemed' counter, and the operator/proof restriction. It adds context about how the Studio renders a progress bar from this counter. It stops short of explicitly stating read-only semantics or pagination behavior, but for a list operation the key behavioral traits are covered.
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 two sentences long, front-loaded with the primary action ('List every coupon this operator has minted'), followed by a useful field detail and a clear restriction. Every sentence earns its place with no filler.
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 (one optional parameter, no nested objects) and has an output schema, which reduces the burden on the description. However, the description leaves the 'dpop_token' parameter unexplained and does not differentiate from the sibling 'brain_list_my_coupons'. It provides adequate context for the core operation but misses these important points.
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 0%, so the description must compensate for the single parameter 'dpop_token'. The description mentions 'requires proof' but never explains that this parameter is the proof token, nor how to obtain or use it. It adds no direct meaning to the parameter, leaving the agent without essential guidance for a required-looking auth token.
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 verb and resource: 'List every coupon this operator has minted (newest first).' It clearly identifies the scope (operator-minted coupons) and ordering (newest first). However, it does not explicitly distinguish itself from the sibling tool 'brain_list_my_coupons', which could be seen as overlapping.
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 includes a usage restriction: 'RESTRICTED to operator — requires proof.' This tells the agent when the tool can be used (by operators with proof) but does not provide explicit guidance on when to use this tool versus alternatives like 'brain_list_my_coupons' or other list tools. No exclusions or alternative pointers are given.
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.