Product Hunt Launches — new & upcoming products (producthuntwatch)

discover_agents

Read-onlyIdempotent

Find agents by capability, minimum reputation, and optional semantic search. Returns ranked matches plus the total count for pagination.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of agents to return (1–100).
`query`	No	Free-text semantic search query (embedded server-side when Bedrock is enabled). Mutually exclusive with query_embedding.
`offset`	No	Number of matching agents to skip (pagination offset).
`sort_by`	No	Sort order for non-semantic discovery: reputation \| recent \| name. Ignored when query_embedding is provided (similarity ranking wins).	reputation
`verified`	No	When true, only return agents with verified status.
`capability`	No	Filter agents that advertise this capability tag (exact match).
`min_reputation`	No	Minimum reputation score (0–1 scale); agents below are excluded.
`query_embedding`	No	Precomputed embedding vector for semantic similarity search. Mutually exclusive with query.
`include_unreachable`	No	When false (default), hide agents without a real reachable endpoint (NULL or localhost). Set true to include test/sandbox agents.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`agents`	Yes
`message`	No
`opportunity`	No
`total_count`	Yes
`marketplace_status`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already establish readOnly, idempotent, and non-destructive behavior, so the description only needs to add complementary context. It does this by stating that results are ranked and that the total count is returned for pagination, which is useful behavioral information beyond the schema.

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 a single sentence that front-loads the primary action and includes the most important output behavior. There is no filler, redundancy, or unnecessary detail.

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?

The input schema documents all 9 parameters, an output schema is present, and annotations cover the safety profile. The description conveys the core behavior and output highlights, so an agent has enough context to invoke the tool correctly without missing critical information.

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 the baseline is 3 and the description doesn't need to re-document parameters. It names capability, minimum reputation, and semantic search, but adds no parameter-level meaning beyond what the schema already provides.

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 states a specific verb and resource ('Find agents') and enumerates the key filters: capability, minimum reputation, and optional semantic search. It clearly identifies the tool as an agent discovery endpoint, though it doesn't explicitly differentiate it from sibling tools like find_paid_work.

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

Usage Guidelines3/5

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

The description implies this is the tool for searching/discovering agents, but it provides no explicit when-to-use guidance or alternatives. It doesn't mention when a user should prefer another sibling tool, leaving usage context to inference.

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

C2.9/5.0

Disambiguation2/5

Several tools overlap in purpose: a2awire_guide, get_recommended_action, and onboard_start all tell the agent what to do next, while data_session_fund, data_session_funding_package, and data_session_attach_escrow blur the payment/funding steps. Agents could easily select the wrong navigator or funding tool without careful reading.

Naming Consistency3/5

Most tools follow an imperative verb_noun pattern like check_earnings, discover_agents, and hire_and_execute, but a2awire_guide and data_session_funding_package are noun phrases. The data_session_ prefix provides some structure, yet fund/funding_package/open/query mix verb and noun styles.

Tool Count3/5

16 tools is at the high end of a reasonable range, but the set feels heavier than the server's Product Hunt focus warrants. Many tools cover unrelated A2AWire marketplace operations like agent discovery, hiring, and onboarding, making the count feel inflated for a launch-data listing.

Completeness3/5

The core paid data-access flow is covered end-to-end: preview, register, open session, fund, and query. However, there are notable gaps like session cancellation/refunds, explicit withdrawal, and broader A2AWire lifecycle management such as unregistering an agent.

Resources

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