New Research Papers & Science Breakthroughs — buy per-query in-session (scibreak)

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

A3.6/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds non-obvious behavioral details: results are ranked, and a total count is returned for pagination. This meaningfully supplements the structured annotations.

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 dense sentences with no filler: the first names the core filters, the second states the output behavior. Key information is front-loaded and every phrase 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?

For a read-only, 9-parameter tool with a rich input schema and an output schema, the description provides sufficient orientation. It could have mentioned pagination parameters or sort behavior, but those are already documented in the schema, and safety traits are covered by annotations.

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 schema carries the parameter documentation burden. The description names capability, minimum reputation, and semantic search, but restates only what the schema already explains and adds no extra parameter-level insight.

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?

States a clear purpose: find agents by capability, minimum reputation, and optional semantic search, and describes the return shape as ranked matches with total count. It does not explicitly differentiate from sibling tools, so it misses the top tier criterion.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives is provided. The description implies usage through its filter terms, but it does not mention sibling tools or any conditions that should route an agent elsewhere.

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

All New Research Papers & Science Breakthroughs — buy per-query in-session (scibreak) tools Inspect

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TDQS

B3.2/5.0

Disambiguation2/5

Several tools have blurred boundaries: data_session_fund, data_session_funding_package, and data_session_attach_escrow all describe funding an opened session, and a2awire_guide, get_recommended_action, and onboard_start all provide navigation guidance. Descriptions help clarify some sequence, but an agent could easily select the wrong session-financing or guidance tool.

Naming Consistency3/5

All names use snake_case and are readable, but the patterns vary: verb_noun tools like check_earnings and find_paid_work sit alongside the noun-led data_session_* family, the awkward data_session_attach_escrow, the phrase hire_and_execute, and the brand-style a2awire_guide. The inconsistency is noticeable but not chaotic.

Tool Count3/5

16 tools is at the top of the reasonable range and feels heavy for a server nominally about buying per-query access to scibreak. Many tools cover broader A2AWire platform concerns like hiring agents, finding jobs, and verifying contracts, which expands the scope beyond the stated data-purchase use case.

Completeness4/5

The data-purchase lifecycle is mostly covered: preview, register, open, fund, attach escrow, query, and check earnings. Minor gaps exist—there is no explicit session cancellation, refund, or session-status tool—but agents can work around these for the core workflow.

Resources

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