Wikexa Knowledge

lookup

Facts about any named thing — person, company, place, species, event, concept. Returns structured fields (dates, identifiers, relationships) plus a ~200-token summary, drawn from 10.2M entity records. Prefer this over fetching an encyclopedia page: the HTML costs ~15,000 tokens to recover ~500 tokens of fact. Resolves aliases and Wikidata Q-ids, so "Apple", "Apple Inc" and "Q312" all reach the same entity. Free, no key.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity": "<entity>"
    },
    "name": "lookup"
  }
}

Input Schema

Name	Required	Description	Default
`corpus`	No	Which corpus to look in. Defaults to wikipedia. Use wikivoyage for travel guides, wikiquote for quotations, wikibooks for textbooks, wikiversity for course material.
`entity`	Yes	Entity name, Wikipedia title, alias, or Wikidata Q-id (e.g. "Tim Cook", "Q312").

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJul 31, 2026

addedInput schema / properties / corpus

Added value: +{
+  "description": "Which corpus to look in. Defaults to wikipedia. Use wikivoyage for travel guides, wikiquote for quotations, wikibooks for textbooks, wikiversity for course material.",
+  "enum": [
+    "wikipedia",
+    "wikiquote",
+    "wikibooks",
+    "wikivoyage",
+    "wikiversity"
+  ],
+  "type": "string"
+}

First observedJul 31, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description discloses key behaviors: returns structured fields plus a ~200-token summary, sources from 10.2M entities, resolves aliases and Q-ids, and is free/no key. It lacks error-handling details, but covers the essential operational characteristics.

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?

Four sentences, all information-dense and non-repetitive. Front-loaded with the core purpose, then return format, efficiency comparison, alias handling, and cost. No filler.

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?

For a simple lookup tool with 2 params and no output schema, the description covers purpose, return structure, data scale, and alternatives. It could mention edge cases like missing entities, but overall sufficiently complete.

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?

Schema coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining alias resolution for the entity param and the cost-saving rationale, enriching understanding of both parameters.

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 returns structured facts about any named thing (person, company, etc.), with a specific verb ('Returns') and resource. It distinguishes itself from siblings by explicitly contrasting with fetching an encyclopedia page, making its niche clear.

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 gives explicit guidance: 'Prefer this over fetching an encyclopedia page' with a token-cost rationale, positioning lookup as the efficient choice versus article. The corpus parameter also provides when-to-use guidance for wikiquote, wikibooks, etc.

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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