Erase all pricing models and restore a viable default.
Deletes every stored model, then self-initializes a fresh one from the tool registry — all tools at 0 sats with proper UUIDs. Returns the new model.
RESTRICTED to operator — requires proof (nsec-signed).
| Name | Required | Description | Default |
|---|---|---|---|
| dpop_token | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations providing a safety profile, the description fully discloses the destructive behavior: every stored model is deleted, a fresh model is self-initialized from the registry, and the new model is returned. It also highlights the need for operator proof, adding valuable transparency.
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 very concise, front-loaded with the primary action, and each sentence adds essential context: the default model, registry details, return value, and access restriction. No fluff or redundant phrasing.
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 description covers the tool's behavior, return value, and access restriction, but leaves the dpop_token parameter entirely undocumented. Since there is an output schema, the return is covered, yet the missing parameter semantics makes the invocation incomplete. The tool also lacks any note about side effects beyond deletion, which is already implicit.
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 sole parameter dpop_token is not mentioned in the description, and schema coverage is 0%. The description mentions 'nsec-signed' proof but does not connect it to the token parameter, leaving the agent to guess how to supply the required proof. This is a significant gap that the description should have filled.
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 uses specific verbs ('Erase', 'restore') and names the resource ('pricing models'), clearly differentiating this reset/wipe tool from siblings like brain_set_pricing_model and brain_get_pricing_model. It states the outcome: restore a viable default with all tools at 0 sats.
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 clearly restricts use to the operator and requires signed proof, giving strong context for when this tool is appropriate. It does not explicitly list alternatives or exclusions, but the destructive reset nature is self-evident, so this is a minor gap.
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.