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Frontier

frontier

Get a ranked queue of recent papers and repos found by your standing queries, prioritizing unseen items and demoting shown ones so each call returns the next batch.

Instructions

The FRONTIER queue: recent artifacts (papers and repos — arXiv preprints, GitHub repos, and published literature across every discipline via OpenAlex) that the user's own standing queries pulled from the outside world, RANKED by how much they deserve attention right now. Read-only and free. Use it when the user asks what is new, what the frontier found, or what they have not looked at yet.

What the ranking is. Recomputed on every call from checkable facts, never stored: how many standing queries found the same artifact (the strongest signal — independent convergence), how many regions of the user's KB asked for those queries, how recent it is, how substantial it is for its kind (stars for a repo, abstract length for a paper — never compared across sources), minus how often it has already been shown.

One artifact is ONE card even when several sources staged it under different ids (the same preprint reached by DOI and by its /abs/ page, a paper deposited twice). Such a card carries duplicate_of naming the ids it stands for — it is a merge, so no signal is lost and total counts artifacts rather than rows.

Nothing is ever filtered out. Every term demotes; none excludes. Each candidate carries a state — "new", "seen" (shown before), "dismissed" (the user said stop), or stage 3's verdict — and a dismissed one still comes back, ranked last and labelled. Report a dismissed item as dismissed; do not hide it from the user and do not re-pitch it.

Calling it again advances the queue. This tool records what it showed you, and being shown demotes — so a second call surfaces the NEXT batch rather than re-pitching the same head. There is no cursor to pass. remaining > 0 means there are more below the cut; call again, or raise limit. Nothing gets stranded: an unseen candidate carries no penalty at all, so it outranks everything already shown.

There is no save step here, and you should not invent one. Admission into the knowledge base is Frontier stage 3's job and it is AUTONOMOUS — it runs on its own schedule, with no approval step and nothing for you to call. A candidate's state tells you what stage 3 has already decided: "materialized" means it is in the knowledge base, "rejected" means the fetch mechanically failed. "rejected" is NEVER a quality judgement — stage 3 has no judge. Report it as a fetch failure, never as "not good enough". Do not tell the user YOU added or kept anything, and do not offer to: nothing you do here admits an artifact, and as of 2026-08-13 there is no tool anywhere that admits one on request — the save_paper / save_repo vault writers were deleted. Stage 3 is the only admission path and it runs on its own.

Params: limit (default 20), dismiss (list of candidate_ids the user explicitly wants stopped — pass ONLY on an explicit request, never on inference), include_dismissed (default True; passing False hides them and reports how many it hid).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
dismissNo
include_dismissedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses that the ranking is recomputed and never stored, that artifacts are merged via duplicate_of, that nothing is filtered out, that calling again advances the queue, and the exact meaning of states like dismissed, materialized, and rejected.

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

Conciseness4/5

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

The description is well-organized and front-loads purpose before diving into ranking, states, and params. However, the no-save/stage-3 warning is repeated across two separate paragraphs and includes a date-stamped tool-deletion detail that is somewhat redundant, making it slightly longer than necessary.

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

Completeness5/5

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

Given that an output schema exists, the description need not spell out return shapes; it covers all calling-relevant context: ranking inputs, merge semantics, state meanings, pagination via remaining, the absence of a save step, and parameter constraints. An agent has enough information to select and invoke this tool correctly.

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

Parameters5/5

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

The input schema only provides names, types, and defaults, so the description adds essential meaning. It explains limit through the remaining/cut mechanics, dismiss as an explicit-only list of candidate_ids, and include_dismissed as a hide-and-report option. This fully compensates for the 0% schema description coverage.

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 opening sentence names a specific resource ('FRONTIER queue') and defines exactly what it returns: recent papers/repos pulled by the user's standing queries, ranked by attention-worthiness. It also states read-only/free and provides concrete user-intent triggers. It clearly differentiates itself from save/admission mechanisms even though it does not name sibling tools.

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

Usage Guidelines5/5

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

It explicitly says when to use the tool: when the user asks what is new, what the frontier found, or what they have not looked at yet. It also gives strong when-not guidance: pass dismiss only on explicit request, do not invent a save step, do not claim admission, and report rejected items as fetch failures rather than quality judgments.

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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