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batch_fetch

Fetch up to 50 research papers in a single batch with bounded concurrency, producing ordered compact summaries and optional persistent manifests for auditable results.

Instructions

Fetch 1..50 papers with bounded concurrency and input-ordered compact manifest/acceptance results. It may access remote services and write the same cache, artifacts, or Markdown as fetch_paper. detail=bounded returns only a batch-wide bounded text sample. Set run_manifest or resume for auditable persistence; overwrite defaults false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modesNo
detailNocompact
resumeNo
queriesYes
strategyNo
overwriteNo
max_tokensNofull_text
concurrencyNo
no_downloadNo
download_dirNo
include_refsNo
prefer_cacheNo
run_manifestNo
artifact_modeNomarkdown-assets
batch_resultsNo
save_markdownNo
content_max_charsNo
continue_on_errorNo
markdown_filenameNo
markdown_output_dirNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNo
routeNo
stageNo
stateNo
traceNo
detailNo
reasonNo
run_idNo
statusNo
abortedNo
detailsNo
resultsNo
summaryNo
providerNo
warningsNo
cancelledNo
persistedNo
retryableNo
candidatesNo
events_pathNo
http_statusNo
missing_envNo
query_countNo
reused_countNo
source_trailNo
error_categoryNo
lane_cooldownsNo
schema_versionNo
attempted_countNo
completion_orderNo
content_max_charsNo
run_manifest_pathNo
request_fingerprintNo
retry_after_secondsNo
content_returned_charsNo
Behavior4/5

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

Annotations already indicate readOnlyHint=false and openWorldHint=true, so the description's mention of 'access remote services' and 'write the same cache, artifacts, or Markdown' adds useful context beyond the annotations. It also discloses the behavioral nuance of detail=bounded ('returns only a batch-wide bounded text sample') and notes persistence options. This is a solid addition without contradicting any 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?

The description is three sentences with no unnecessary words. It front-loads the core action and scope, then adds key parameter guidance. Every sentence provides distinct value, making it both concise and well-structured.

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?

Despite having an output schema and annotations, the description is too brief for a tool with 20 parameters and nested objects. It fails to mention many critical aspects like modes, strategy, concurrency limits, or how batch results are returned. While the output schema covers return values, the tool's breadth demands more context to be considered complete.

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

Parameters2/5

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

The schema is large (20 parameters) and has 0% description coverage, so the description must compensate. It only explains detail, run_manifest, resume, and overwrite, leaving the remaining 16 parameters (e.g., modes, strategy, concurrency, no_download) completely unexplained. This is a significant gap for a complex tool with nested objects and many options, so the description adds only minimal parameter-level meaning.

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 clearly states the function: 'Fetch 1..50 papers with bounded concurrency and input-ordered compact manifest/acceptance results.' It uses a specific verb ('Fetch') and resource scope (papers), and distinguishes itself from related tools by mentioning it shares effects with fetch_paper but operates as a batch process. This makes its purpose unambiguous and differentiated from siblings.

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 provides usage context by noting it 'may access remote services and write the same cache, artifacts, or Markdown as fetch_paper,' implying it's an alternative for batched fetches. It also gives parameter-level guidance (e.g., 'Set run_manifest or resume for auditable persistence'). However, it does not explicitly state when to use this tool over others, nor does it list exclusions or specific scenarios, so it falls short of a 5.

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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