Skip to main content
Glama

l402-search

Read-only

Find paid APIs on Nostr by searching kind 31402 announcements. Filter by topic and payment method, then get URLs, pricing, and capabilities from relay results.

Instructions

Search Nostr relays for paid API announcements (kind 31402) and return their URLs, pricing and capabilities. Topic and payment-method filters are sent to the relays; the query text is matched locally. Announcement text is written by whoever published it and is marked as untrusted content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query to match against service names, descriptions, and capabilities
relaysNoNostr relay URLs to query (defaults to popular public relays)
topicsNoFilter by topic tags (e.g. ["ai", "data"])
timeoutNoRelay subscription timeout in milliseconds (default 5000)
maxResultsNoMaximum number of results to return (default 20)
paymentMethodNoFilter by payment method (e.g. "l402", "cashu", "x402", "xcashu")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.2.1

TDQS

A4/5.0
Behavior4/5

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

Annotations declare readOnlyHint and openWorldHint, so the read-only nature is already known. The description adds useful behavioral context beyond annotations: filters are sent to relays, query text is matched locally, and announcement content is untrusted. This is meaningful additional context for an agent.

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 three concise sentences that front-load the core purpose, add relevant filtering mechanics, and include an important trust caveat. There is no redundant or filler content.

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

Completeness4/5

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

With no output schema, the description adequately states what the tool returns ('URLs, pricing and capabilities') and covers key behavioral details like local matching and untrusted content. It is not exhaustive, but it provides enough for an agent to select and invoke the tool correctly.

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 description coverage is 100%, so parameters are already well-documented by the schema itself. The description adds related context, such as local matching of query text and relay-side filtering, but does not significantly expand on the parameter semantics beyond the schema.

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 identifies a specific action ('Search Nostr relays for paid API announcements (kind 31402)') and the returned content ('URLs, pricing and capabilities'). It does not explicitly distinguish this from sibling tools like l402-discover, so it falls short of full sibling differentiation.

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 for when this tool is appropriate: searching Nostr relays for paid API announcements. It does not explicitly name alternatives or exclusions, but the use case is specific enough for an agent to understand when to invoke it.

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