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Loyal Spark

by io.github.aspekt19

bazaar_discover_resources

Discover third-party x402-paid resources published in Coinbase CDP's Bazaar (docs, data feeds, AI inference, etc). Read-only. Filter by free-text q and/or network (e.g. 'base').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text filter matched against the resource JSON (name/description/url)
limitNoMax rows returned (default 25, max 100)
cursorNoPagination cursor from a previous call
networkNoFilter by network, e.g. 'base' or 'base-sepolia'

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses read-only behavior and mentions filtering, which is helpful, but it does not detail pagination behavior, response structure, or any permissions or rate limits.

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

Conciseness5/5

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

The description is two sentences, front-loaded with the primary purpose, and every sentence adds value. There is no redundant or unnecessary content.

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

Completeness3/5

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

The tool is simple with only four optional parameters, and the description covers purpose and filtering. However, without an output schema, the description does not explain what the response contains, and it does not explicitly distinguish from the sibling discovery tool for MCP servers.

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

Parameters3/5

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

Schema coverage is 100%, with each parameter described in the input schema. The description's mention of 'free-text q and/or network' adds no new meaning beyond the schema, so the baseline of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool discovers third-party x402-paid resources in Coinbase CDP's Bazaar, with examples like docs, data feeds, and AI inference. It is specific about the resource type but does not explicitly differentiate from the sibling bazaar_discover_mcp_servers, which is a related but distinct purpose.

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?

The description provides clear context: it is a read-only discovery tool with filtering by free-text query and network. No explicit alternatives or exclusions are mentioned, but the usage scenario is sufficiently clear.

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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TDQS

B3.4/5.0
Disambiguation3/5

Several tools have overlapping purposes, such as mint_loyalty_tokens vs earn_points (both mint loyalty tokens with fee bundles), create_loyalty_program vs register_loyalty_program (deploy vs register existing token), and activate_loyalty_program vs update_program_status (both manage program status). Some pairs like check_voucher_status and list_gift_certificates also overlap on voucher/certificate tracking. However, descriptions are detailed enough to reduce ambiguity for careful agents.

Naming Consistency4/5

Most tools follow a consistent verb_noun snake_case pattern (e.g., create_reward, list_loyalty_programs). Subtle deviations include earn_points vs mint_loyalty_tokens (different verbs for similar mint operations) and use_voucher vs redeem_reward (different verb styles for redemption). Overall, the naming is predictable and understandable.

Tool Count2/5

With 39 tools, this server is bloated for a loyalty platform. The addition of Bazaar discovery/payment tools and report management expands the scope, but many tools overlap or cover minor variations (e.g., two workflow planners: generate_program_defaults and get_program_workflow_status). A leaner set of 20-25 tools would be more appropriate.

Completeness4/5

The tool surface covers the full loyalty program lifecycle: creation, activation, registration, token minting/transfer, rewards, gift certificates, vouchers, offers, customer export, analytics, and reports. Notable gaps include no CRUD for personalized offers (only create), no edit capability for rewards (only status changes), and no direct function to list all vouchers by merchant (only status check by code). These are minor workarounds.

Resources