BridgeNode MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only say readOnlyHint is false, but the description adds substantial context: the x402 payment requirement, the initial 402 response with a price, the need to retry with payment metadata, non-streaming responses, model wins over mode if both are provided, and max_tokens defaulting to 4096 with clamping. These behaviors are not expressed anywhere in the schema or annotations.

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 front-loaded: purpose first, then the payment flow, then parameter rules, ending with guidance. It is somewhat long, but each sentence adds new, necessary information and does not repeat schema content. It is appropriately dense for a tool with this much behavioral nuance.

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 entire call flow including payment, model/mode exclusivity, default and clamped max_tokens, and points to where to inspect models and prices. With an output schema present and a straightforward request/response shape, nothing an agent needs to invoke this tool correctly is missing.

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?

While schema coverage is 100%, the description adds real meaning beyond the schema's short labels: it explains the mode values ('auto = cheapest model fitting the context, eco = cheapest available, premium = best model'), states that model wins if both are sent, and introduces max_tokens default and clamping behavior. This is genuinely informative, not a restatement of the 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 states a specific verb and resource: 'Send a chat completion request to any supported model.' It clearly differentiates itself from the list_models sibling with 'Use this tool to generate text; to inspect models and prices first, use the free list_models tool.' The purpose is unambiguous and distinct from all siblings.

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 explicitly states when to use this tool vs alternatives: 'Use this tool to generate text; to inspect models and prices first, use the free list_models tool.' It also lays out the two-step payment workflow — the first call returns 402 with exact price, and the retry must include _meta['x402/payment'] — so the agent knows exactly when and how to call it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With annotations already declaring readOnlyHint and idempotentHint, the description still adds new behavioral facts: computed locally from server pricing, zero external calls, no state changes, no payment or auth needed. It even explains the mechanism behind idempotence ('computed locally from server pricing config, so repeated calls with identical inputs return identical results') rather than merely echoing the annotation, plus the 'model wins' precedence rule when both mode and model are sent.

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?

Roughly 150 words but every clause carries information: purpose, cost/auth profile, behavioral guarantees, usage routing, parameter semantics, and enum definitions. Nothing repeats the schema (no type resurfacing of 'string' or 'integer'), and the most decision-relevant fact ('free, no payment') is front-loaded in the first sentence. The structure flows from what → behavior → when to → how-to, which is an optimal order for an agent.

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?

All context dimensions are covered: the output schema captures the exact shape of the returned estimate, annotations capture safety/idempotence, and the description covers scope (USDC, chat completion requests), usage context (before chat_completions), auth, precedence, and mode semantics. The one thing it does not spell out, the rounding/unit details of the estimate, is legitimately delegated to the output schema. Nothing an agent needs to call this correctly or decide to route to it is missing.

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 coverage is 100%, so baseline is 3; the description added — deepens this by defining each mode enum value ('auto = cheapest model fitting the context, eco = cheapest available, premium = best model') which the schema's bare enum enumations (auto/eco/premium) leaves undefined." The mutual-exclusion of model vs mode and the 'two are required; if both are sent, model wins' precedence are spelled out in plain language. It doesn't elaborate on the messages or max_tokens meaning, but those are already well described in the 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?

Opens with a specific verb-plus-resource statement: 'Estimate the USDC cost of a chat completion request before paying.' It also differentiates from both siblings — chat_completions is explicitly the 'paid' execution tool this one precedes, and list_models is referenced as the source of model IDs. No ambiguity about what the tool returns or why it exists.

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?

States the exact trigger condition: 'Use this tool to check the exact price for a given model/mode, messages, and max_tokens before calling the paid chat_completions tool.' This explicitly positions it as a pre-flight step to the only sibling it competes with, and implies the counter-condition (don't use it when you intend to execute a chat). The 'free, no payment, no authentication' framing reinforces why it's safe to call first.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds meaningful behavioral detail beyond annotations: free/no payment, no authentication required, any arguments are ignored, repeated calls return identical results, no filtering/pagination/configuration, and data source is local server config. The read-only and idempotent annotations are reinforced and expanded with practical operational context, enabling the agent to predict call behavior confidently.

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?

Although relatively detailed, every sentence earns its place: free/auth status, read-only/idempotent behavior, parameter handling, return content, use case, and explicit exclusions. The core purpose and no-arguments fact are front-loaded, and the organization flows naturally from behavior to usage guidance.

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?

For a zero-parameter list tool with an explicit output schema and rich annotations, the description fully covers what an agent needs: access requirements, return content, side-effect safety, the canonical endpoint reference, and disambiguation from both sibling tools. Nothing essential is missing.

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 tool takes zero parameters, and the schema coverage is 100%, so the baseline is 4. The description adds useful semantics by explicitly confirming that any arguments are ignored and that calling without arguments returns the full catalog. This prevents agents from attempting to pass meaningless parameters.

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?

Names a specific verb and resource ('List available models and their prices') and clearly separates itself from siblings by stating it returns the catalog rather than generating text or estimating cost. The purpose is unambiguous and differentiates this tool from chat_completions and get_price_estimate.

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?

Explicitly states when to use it ('inspect models and prices before calling the paid chat_completions tool') and when not to use it ('do not use it to generate text' or 'estimate cost'), naming the appropriate alternatives in both exclusions. The usage guidance is direct and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.