Skip to main content
Glama

infobox

Extract a Wikipedia article's structured fact box as a plain-text field/value table. Get dates, people, places, and statistics without prose; citations stripped, links flattened, and absence reported.

Instructions

Extract the structured fact box (infobox) from a Wikipedia article as a field/value table — dates, people, places, statistics, founders, CEOs, populations, capitals. The fastest path to a concrete fact ('who founded X?', 'what's the population of Y?') without wading through prose. Renders wikitext into clean plain text (citations stripped, links flattened, fields capped at 50). Use this for facts; use summary for the prose gist and article_extract for full text. Reports clearly when an article has no infobox.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoWikipedia language code (default 'en')en
titleYesArticle title (e.g. 'Albert Einstein' or 'Paris')

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.3

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond the schema by stating that wikitext is rendered into clean plain text, citations are stripped, links are flattened, fields are capped at 50, and missing infoboxes are reported. This is rich, non-obvious behavioral context.

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 front-loaded with the core action and output format, followed by concrete examples, behavioral details, and routing to alternatives. Every sentence earns its place with no filler. It is rich but appropriately compact.

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?

For a simple 2-parameter tool with 100% schema coverage and no output schema, the description fully explains what is returned, how it behaves, what happens when no infobox exists, and when to prefer sibling tools. Nothing needed for correct invocation is missing.

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 100%, so the schema already documents both `title` and `lang`. The description adds context about Wikipedia articles and example questions, but it does not add meaningful parameter-level detail beyond the schema. The baseline of 3 is appropriate.

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 opens with a specific verb and resource: 'Extract the structured fact box (infobox) from a Wikipedia article as a field/value table.' It names concrete content types and example questions, and it explicitly distinguishes itself from summary and article_extract. An agent can clearly tell what this tool does and how it differs 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 Guidelines5/5

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

The description explicitly says 'Use this for facts; use `summary` for the prose gist and `article_extract` for full text.' This gives direct selection guidance, names the alternatives, and states the condition for choosing this tool. No inference is required.

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