Set the active pricing model. RESTRICTED to operator.
Requires a valid proof (Schnorr-signed kind-27235 event) proving the caller holds the operator's nsec.
| Name | Required | Description | Default |
|---|---|---|---|
| dpop_token | No | ||
| model_json | Yes |
| 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 provided, the description carries the full burden of behavioral disclosure. It explicitly states the critical behavior: a valid Schnorr-signed proof is required to prove operator status. This is a key behavioral trait that is not captured elsewhere. However, it does not describe other behaviors such as success/failure responses, side effects, or reversibility.
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 only two sentences, with the primary action front-loaded in the first sentence. Every word earns its place: the verb 'Set' and resource 'active pricing model' are immediately clear, and the second sentence provides critical auth context without fluff.
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 presence of an output schema (although not detailed) and the important auth requirement disclosure, the description covers the most critical aspect. However, it lacks details on parameter semantics (what 'model_json' should contain, how to pass the proof via 'dpop_token') and does not set expectations about return values or possible errors. A more complete description would include these to support safe invocation.
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 input schema has 0% description coverage, meaning the description must compensate by explaining parameters. The description mentions 'model_json' implicitly (the pricing model being set) but does not clarify its format or expected content. The 'dpop_token' parameter is not even mentioned, despite its likely role in the proof requirement. The description adds almost no value beyond the raw schema.
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 action: 'Set the active pricing model.' It also immediately adds the restriction 'RESTRICTED to operator,' which helps distinguish this mutation tool from read-only siblings like 'weather_get_pricing_model' and even from 'weather_reset_pricing_model' (which likely performs a different 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 an operator and requires a valid proof. However, it does not provide guidance on when to use this tool versus alternatives (e.g., using 'weather_get_pricing_model' to read current model first) or when not to use it (e.g., if not operator). The restrictions are mentioned but not framed as explicit usage guidelines.
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.