Generate a Merkle inclusion proof that a patron's balance was included in a Bitcoin-notarized snapshot.
| Name | Required | Description | Default |
|---|---|---|---|
| npub | Yes | The patron's Nostr public key (npub1...). | |
| notarization_id | Yes | The notarization record ID. |
| 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 must carry the full burden of behavioral disclosure. It explains the operation (compute a proof from a snapshot) but does not disclose whether it is read-only, any prerequisites (e.g., existing notarization), error conditions, or what the proof can be used for. It implies a safe computation but lacks explicit guarantees.
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 a single, well-structured sentence that leads with the verb and object, immediately conveying the tool's purpose without any filler or redundant information. It earns its place with every word.
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 tool's simplicity (two required parameters, no nested objects, and an output schema present), the description adequately covers what the tool does. However, it could be more complete by explicitly stating when to use it relative to sibling proof-related tools, but this is not a critical gap for a straightforward proof generation tool.
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?
Since schema coverage is 100% and both parameters have clear descriptions (npub as Nostr public key, notarization_id as record ID), the description adds minimal semantic value beyond linking the two parameters: the proof is for a specific patron and a specific notarization. No additional format or constraint details are provided.
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 generates a Merkle inclusion proof that a patron's balance was included in a Bitcoin-notarized snapshot. The verb 'generate' and the specific resource 'Merkle inclusion proof' distinguish it from related tools like brain_notarize_ledger (creates the snapshot) and brain_check_proof_status (checks status).
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 implies the tool is used when one needs a proof of inclusion for a patron's balance in a notarized snapshot, but it does not explicitly state when to use it over alternatives such as brain_check_proof_status or brain_receive_npub_proof. No exclusions or comparable scenarios are mentioned.
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.