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ListSmith MCP

by QuincyGB
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Python
Remote

list_fix

Repairs one x402 route's Bazaar discovery metadata from a real sample output and returns a validator-passed discovery package with schema, description, OpenAPI snippets, and paste-in instructions.

Instructions

Repair one x402 route's Bazaar discovery metadata.

Calls ListSmith POST /v1/fix ($0.50 USDC on Base via x402).
Returns a complete repaired discovery package — a full output
schema inferred from your sample, a sub-500-char description with
a use-when sentence, OpenAPI and well-known snippets, and paste-in
instructions. The package ships only if it passes both x402 SDK
validators.

Args:
    resource_url: Full URL of the x402 resource to repair.
    sample_output: One real response body from the endpoint
        (required). The repaired output schema is inferred from
        this sample only — nothing is invented.
    name: Optional display name for the generated description;
        derived from the URL when omitted.
    category: Optional category (e.g. token-risk, search, data,
        media, monitoring) used to phrase the description's
        use-when sentence.

Input Schema

NameRequiredDescriptionDefault
nameNo
categoryNo
resource_urlYes
sample_outputYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses the $0.50 USDC cost on Base via x402 payment, that the package is gated on passing both x402 SDK validators, and that the output schema is inferred from the sample only with nothing invented. These are exactly the non-obvious traits an agent needs before invoking a paid mutation-style tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then cost/endpoint, then the return contract, then args — good ordering with no filler. The Args block is somewhat verbose for a tool whose parameter names already self-document, but the extra semantics justify most of it.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers cost, payment rail, required inputs, output package contents, and the validation gate; an output schema exists so return-value detail needn't be restated. Nothing an agent needs to invoke this correctly appears missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it documents all four parameters: resource_url as the target, sample_output as required and the sole source of schema inference, name as optional and derived from the URL when omitted, and category with concrete example values. This is materially more than the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Repair one x402 route's Bazaar discovery metadata,' and pins the underlying call to ListSmith POST /v1/fix. An agent can tell this is the remediation action, distinct from the sibling list_grade evaluation tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies the use case clearly (repairing broken/missing discovery metadata) and specifies the required input discipline (one real sample response). It never explicitly says when not to use it or points to list_grade as the alternative, so it stops short of full routing guidance.

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

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