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Glama

Get Institution

get_institution_context
Read-onlyIdempotent

Fetch agent-oriented context for a Lacuna institution to analyze research output, with a compact top-author view or full details for broader institution review.

Instructions

Fetch agent-oriented context for a Lacuna institution.

view selects the response shape:

  • "context" (default, recommended): compact institution context — capped top authors with the duplicated institution block dropped server-side.

  • "full": the complete institution context shape (duplicated institution record and a bounded author list with explicit truncation metadata).

Use get_institution_authors to page through the complete author list.

Input Schema

NameRequiredDescriptionDefault
viewNocontext
institution_key_or_urlYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety profile is covered. The description adds genuine behavioral detail beyond that: the compact view drops the duplicated institution block server-side, and the full view exposes explicit truncation metadata — useful for reasoning about response size and completeness.

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?

Front-loaded with the core action, then a clean bulleted breakdown of the view values, closing with the sibling pointer. The prose is a bit exposition-heavy in the two bullets but every sentence carries actionable information.

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?

An output schema exists, so return values needn't be spelled out, yet the description still characterizes each response shape well. The only real gap is the undocumented institution identifier parameter, which the agent must interpret from the name alone.

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 0%, so the description carries the burden. It fully documents 'view' (both enum values, default, recommended choice), but says nothing about institution_key_or_url — whether it accepts a slug, key, or URL. One of two parameters is well covered; the other is left to inference.

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 specific verb (Fetch) and resource (agent-oriented context for a Lacuna institution), and distinguishes itself from get_institution_authors by naming that sibling's role for paging. The phrase 'agent-oriented context' is slightly abstract, but the view parameter explanation resolves most ambiguity.

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?

Gives explicit guidance for choosing between the two view modes, flags 'context' as the recommended default, and routes the agent to get_institution_authors for complete author pagination. It lacks guidance on when not to use this tool (e.g., vs get_author_context or search_lacuna), so it stops short of a full 5.

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