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

data_preview

Read-onlyIdempotent

✅ No API key needed — call this now. Listing: scibreak: New Research Papers & Science Breakthroughs. Price 0.01 USDC/query (max 20 queries/session). Sample questions: What are the newest AI research papers?; Any new astrophysics papers this week?. FREE preview — no key, no payment. Try one of the sample questions now.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	No	Public listing slug. Defaults to the routed session's listing when connected via /mcp/data/{slug}/http.
`question`	No	Optional free-text question you'd ask this data (echoed back).

Schema Changelog

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

First observedSep 6, 2026

TDQS

B3/5.0

Behavior2/5

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

The description adds useful context about not needing an API key and being free, but it also includes 'Price 0.01 USDC/query' while claiming 'FREE preview — no key, no payment', creating ambiguity about whether this call costs money. It also fails to disclose that only public metadata is returned, leaving that to the schema description.

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

Conciseness2/5

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

The description is repetitive ('No API key needed' and 'FREE preview — no key, no payment' say essentially the same thing) and mixes irrelevant pricing details into a supposedly free preview. It is not tightly structured around the tool's function.

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

Completeness2/5

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

With no output schema, the description should state what the caller receives, but it only implies a preview and leaves return behavior to the schema description. The unresolved pricing ambiguity and lack of a clear statement of what is returned make it incomplete, though the tool is simple and annotations cover safety.

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

Parameters4/5

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

Schema coverage is 100% with clear descriptions for both slug and question. The description adds value by providing concrete sample question values and encouraging their use, which helps an agent understand what the question parameter is for.

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 identifies a free preview of the scibreak listing and invites the user to try sample questions, so an agent can infer the tool previews listing data. However, it reads more like promotional copy than a precise verb+resource statement and does not explicitly say 'returns public metadata for a listing'.

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 usage as a free, no-key entry point ('No API key needed — call this now', 'FREE preview'), but it never names alternatives such as data_session_open or data_session_query, nor does it state when not to use this tool. Sample questions provide practical examples but no explicit selection criteria.

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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