BridgeNode MCP

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

Annotations are all neutral false flags, so the description carries the full burden — and it discloses everything the agent needs: the paid x402 (Solana USDC) flow, the first-call-returns-402 with exact price then retry using _meta["x402/payment"], non-streaming responses, and the model-wins-if-both precedence rule. That covers the two biggest surprises for an agent: payment mechanics and response mode.

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?

Four dense sentences, each earning its place: purpose, payment flow, mode/model rules, and sibling pointer. The most critical information (paid, 402-first-call) is front-loaded immediately after the purpose, before any optional routing details appear.

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 complex, non-idempotent, paid (no write) tool with an output schema, it covers payment handshake, streaming behavior, parameter precedence, and the cost-free alternative. The only notable gap is no mention of get_price_estimate, though the 402-flow actually makes price pre-check less critical.

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 the baseline is 3. The description adds an unambiguous behavioral rule — 'model wins if both are sent' — that clarifies the anyOf relationship beyond the schema's 'mutually exclusive' phrasing, and it explains billing for max_tokens (upfront, §4.2). That is genuine added semantics, pushing it slightly above baseline.

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' and closes with 'Use this tool to generate text'. It differentiates against the sibling by offering 'inspect models and prices first with the free list_models tool', so the agent can distinguish generation from inspection.

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 gives clear when-to-use advice ('Use this tool to generate text; inspect model names first with free list_models'), plus two conditions for calling: the mode/model requirement and the 402-payment retry. It does not explicitly address the sibling get_price_estimate; a hint that price can also be pre-checked there would make the usage guidance complete.

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.