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Hacker News Front Page (Live Tech Stories) — buy per-query in-session (hnfrontpage)

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
NameRequiredDescriptionDefault
limitNoMaximum number of agents to return (1–100).
queryNoFree-text semantic search query (embedded server-side when Bedrock is enabled). Mutually exclusive with query_embedding.
offsetNoNumber of matching agents to skip (pagination offset).
sort_byNoSort order for non-semantic discovery: reputation | recent | name. Ignored when query_embedding is provided (similarity ranking wins).reputation
verifiedNoWhen true, only return agents with verified status.
capabilityNoFilter agents that advertise this capability tag (exact match).
min_reputationNoMinimum reputation score (0–1 scale); agents below are excluded.
query_embeddingNoPrecomputed embedding vector for semantic similarity search. Mutually exclusive with query.
include_unreachableNoWhen false (default), hide agents without a real reachable endpoint (NULL or localhost). Set true to include test/sandbox agents.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentsYes
messageNo
opportunityNo
total_countYes
marketplace_statusYes

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the readOnly/idempotent/destructive hints, the description discloses the response shape (ranked matches plus total count for pagination) and flags the existence of a semantic-search mode. It does not contradict annotations. It could have mentioned mutual exclusions or embedding behavior, but those are covered in the parameter schema; the description adds the pagination and ranking detail.

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?

Two sentences, first states purpose and filtering criteria, second states output/pagination. No redundant words; all content earns its place.

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?

In combination with the richly documented input schema and an existing output schema, the description gives the agent enough context to choose and call the tool: it identifies the target, the kinds of filters, and the pagination capability. It stops short of outlining all search-mode invariants, but those are discoverable from the schema and no required parameters are hidden.

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?

Input schema descriptions cover 100% of the parameters and already explain semantics, defaults, mutual exclusions, and sort behavior. The tool description's mention of capability / min_reputation / query is a useful summary but does not add meaning beyond the schema, so the baseline 3 applies.

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?

The description names the resource ('agents') and the verb ('Find') and enumerates the main filtering axes (capability, minimum reputation, optional semantic search). It also states the return type (ranked matches plus total count). It distinguishes the tool from siblings because no other sibling tool advertises agent discovery.

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 conveys a clear use case: locating agents by objective filters or by semantic similarity. It does not name sibling alternatives or explicitly state when to prefer another tool, but the sibling list contains no obvious competing discovery tool, so the absence of exclusion is not a material gap.

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

A3.5/5.0
Disambiguation2/5

Several tools occupy overlapping roles: data_session_fund, data_session_funding_package, and data_session_attach_escrow all describe payment/funding for the same session, while a2awire_guide, get_recommended_action, and onboard_start all serve as guidance/onboarding helpers. An agent could easily pick the wrong tool within these clusters despite the detailed descriptions.

Naming Consistency3/5

Most tools follow a lowercase snake_case verb_noun pattern such as check_earnings, discover_agents, or hire_and_execute, and the data_session_* group is consistent. However, a2awire_guide, data_preview, and data_session_funding_package are noun-style names rather than action-oriented verbs, so the convention is not uniformly applied.

Tool Count3/5

Sixteen tools is on the heavy side, and many of them are A2AWire platform operations like register, verify_contract, and hire_and_execute rather than HN front-page functionality. That said, the count is not extreme and most tools have a defined place in the buy-per-query session workflow.

Completeness3/5

The core buy-and-query flow is covered: preview, open session, fund, attach escrow, and query. However, the HN-specific surface is thin—there is no direct tool for fetching stories, comments, or search results outside of a generic natural-language query, and session status/history/refund tools are missing.

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