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get_author_context

Read-onlyIdempotent

Fetch a Lacuna author's research context: read their papers, research directions, and impact, with options for full detail and similar authors.

Instructions

Fetch agent-oriented context for a Lacuna author.

Author profiles describe research output (papers, directions, impact). A free-text affiliation field may be present but can be incomplete or outdated and may not represent current employment; the corpus has no biography or employment history, so do not infer those from this data.

view selects the response shape:

  • "context" (default, recommended): Lacuna's compact author context — capped readable papers and an impact_directions list (named research directions) in place of the raw numeric impact_clusters telemetry, with the duplicated nested author record dropped server-side.

  • "full": the bounded full-shape author context (raw impact_clusters, nested author record; server collections are capped at 100).

Set include_neighbors=True to include similar authors as named, linkable records. Neighbor computation may add significant server latency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewNocontext
author_id_or_urlYes
include_neighborsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: caveats about affiliation data being incomplete/outdated, no biography/employment history, latency implications of include_neighbors, and server-side collection caps (100). This significantly exceeds what annotations alone provide.

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-structured: a lead sentence, a data-caveat paragraph, and a bulleted view explanation. Every section earns its place, though the affiliation caveat could be slightly more concise without losing meaning. It is appropriately sized for the complexity.

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?

The description is complete for this tool's complexity: it explains response shape variants, data reliability limits, server caps, and neighbor latency. Since an output schema exists, it need not describe return fields in detail. No major gaps are evident.

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?

The input schema has 0% description coverage, so the description must compensate. It thoroughly explains the 'view' parameter with distinct response shapes and the 'include_neighbors' parameter with latency trade-offs. The 'author_id_or_url' parameter is self-explanatory but lacks format details, which is a minor gap.

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 tool 'Fetch agent-oriented context for a Lacuna author' with a specific verb and resource. It distinguishes from sibling tools by describing a composite context (papers, directions, impact) rather than a single aspect like papers or directions.

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 clear context for when to use this tool, especially through the view parameter choices ('context' recommended vs 'full') and the include_neighbors option. However, it does not explicitly name alternative sibling tools for specific needs, though the context implies this is the go-to for a holistic author profile.

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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