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?
No annotations are provided, so the description fully discloses the destructive behavior (erasing all models), the initialization process, and the returned model. It also notes the access restriction. Slightly more detail about potential side effects could push it to 5.
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 earning its place: first sentence states the purpose, second explains the process, third notes the restriction, and fourth implies the return. It is front-loaded and efficiently conveys essential information.
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 complexity (destructive reset with auth) and that an output schema exists, the description covers behavior, restriction, return value, and necessary prerequisites. No further details are needed for an AI agent to invoke it 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?
The sole parameter dpop_token is not explained in the description, and schema coverage is 0%. While the description mentions 'requires proof (nsec-signed)', it does not connect this to the parameter, leaving ambiguity about how to provide the proof. The description adds limited semantic value for the parameter.
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 erases all pricing models and restores a viable default, with specific details about initialization. It distinguishes itself from siblings like weather_get_pricing_model and weather_set_pricing_model by being a reset/reinitialize action.
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 specifies that the tool is restricted to operators and requires a signed proof, providing clear context for when to use. However, it does not explicitly compare with alternative tools like weather_set_pricing_model for partial updates.
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.
Most tools have clearly distinct purposes with detailed descriptions, and the few similar pairs (e.g., check_balance vs check_authority_balance, account_statement vs account_statement_infographic) are well-differentiated by their descriptions. However, some overlapping concepts like forget_credentials vs delete_patron_credential/delete_operator_credential could still cause misselection without careful reading.
All tools share the misleading 'weather_' prefix, which does not reflect their actual domain (billing, credentials, coupons, notarization). Naming patterns are inconsistent, mixing verb_noun (check_balance, list_coupons) with noun-ish names (account_statement, current, forecast) and varied verbs (get, list, check, request, receive, update, delete, forget, mint, redeem, etc.).
With 52 tools, this is an extremely large surface for a sample server. Even though the domain is broad, this count far exceeds the typical well-scoped MCP server and creates unnecessary complexity for agents to navigate.
The tollbooth/billing domain is well-covered: credit purchasing, coupons, credentials, proofs, pricing models, notarization, and operator/patron status. Minor gaps exist (e.g., no single-coupon getter, no direct patron list), but core workflows have no dead ends. The weather aspect is thin with only three tools, but that seems intentional as an example paid service.