Wiki Search

Available as MCP tools: wiki_search + wiki_read — call directly from Claude / Cursor / any MCP client. See MCP Server for the 60-second setup.

Live 1 credit · search free · read

What it does for your agent

wiki_search returns the most relevant Quant Wiki entries for a natural-language query — concepts, formulas, factors, and strategies. Use it as a research entry point: when an agent encounters a finance term mid-task (“what’s the Black-Scholes assumption?”, “explain pairs trading”), call wiki_search first to locate the right wikiItemIds, then wiki_read to load the full markdown body. The hybrid ranker blends semantic similarity with keyword matching — so terse jargon and verbose questions both retrieve well.

Agent flow

Response

`wiki_search` response

WikiItem[]

required

Ranked array of wiki items (highest relevance first).

Show WikiItem fields

string

required

Stable identifier. Pass to wiki_read for the full article.

string

required

URL-friendly slug derived from the title.

string

required

Article title.

string

required

LLM-generated 2–3 sentence summary. Use this to decide whether to load the full article before spending a wiki_read.

string[]

required

Topic tags (e.g. equity, factor, derivatives).

object

required

Hybrid relevance scores. combined is the blended final score; semantic and lexical are the components (all 0–1).

number

Credits consumed by this call (always 1 for search).

number

Account credits remaining.

200 OK · wiki_search

{
  "data": [
    {
      "wikiItemId": "11111111-1111-4111-8111-111111111111",
      "slug": "momentum-factor",
      "title": "Momentum Factor",
      "summary": "Momentum captures the tendency of recent winners to keep winning over horizons of 3–12 months. It is one of the canonical Fama-French factors and underpins many systematic equity strategies.",
      "tags": ["equity", "factor"],
      "scores": { "combined": 0.91, "semantic": 0.88, "lexical": 0.79 }
    }
  ],
  "meta": { "creditsUsed": 1, "remainingCredits": 99 }
}

`wiki_read` response

WikiItem

required

The full wiki entry.

Show WikiItem fields

string

required

Stable identifier.

string

required

Article title.

string

required

2–3 sentence summary.

string

required

Full article body in Markdown. Truncated to maxLength characters if specified.

string

required

One of concept, formula, strategy, factor.

string[]

required

Topic tags.

string

required

Canonical URL on quant-wiki.com.

datetime

required

ISO-8601 last-updated timestamp.

number

Always 0 for read.

number

Account credits remaining.

Notes

Two-step lookup is canonical: search returns IDs + summaries cheaply (1 credit, multiple hits), then wiki_read loads only the article(s) the agent actually needs (free). Read top-1 by default; read top-2 only when summaries are ambiguous.

Use maxLength on wiki_read to preview a long article before spending agent context tokens on the full body.

Queries longer than 2,000 characters are rejected with 400. Truncate or summarize long agent context before calling.

topK is capped at 10 for wiki_search. Higher values are silently clamped.

Direct invocation

HTTP / SDK examples

// 1) Search
{
  "method": "tools/call",
  "params": {
    "name": "wiki_search",
    "arguments": { "query": "momentum factor", "topK": 5 }
  }
}

// 2) Read top hit
{
  "method": "tools/call",
  "params": {
    "name": "wiki_read",
    "arguments": { "wikiItemId": "11111111-1111-4111-8111-111111111111", "maxLength": 1500 }
  }
}

import os, requests

base = "https://api.llmquantdata.com"
headers = {"Authorization": f"Bearer {os.environ['LLMQUANT_API_KEY']}"}

# 1) Search
hits = requests.post(
    f"{base}/api/wiki/search",
    headers=headers,
    json={"query": "momentum factor", "topK": 5},
).json()["data"]

# 2) Read top hit
top = requests.get(
    f"{base}/api/wiki/items/{hits[0]['wikiItemId']}",
    headers=headers,
    params={"max_length": 1500},
).json()["data"]
print(top["body_markdown"])

# 1) Search
curl -X POST "https://api.llmquantdata.com/api/wiki/search" \
  -H "Authorization: Bearer $LLMQUANT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "momentum factor", "topK": 5}'

# 2) Read top hit (replace this sample UUID with wikiItemId from step 1)
WIKI_ITEM_ID="11111111-1111-4111-8111-111111111111"
curl "https://api.llmquantdata.com/api/wiki/items/${WIKI_ITEM_ID}?max_length=1500" \
  -H "Authorization: Bearer $LLMQUANT_API_KEY"

Full parameter reference

wiki_search — request parameters

string

required

Natural-language search query. Max 2,000 characters.

number

default:5

Maximum results to return. Range 1–10.

wiki_read — request parameters

string

required

The wikiItemId returned by wiki_search.

number

Maximum character length for body_markdown. Useful for previewing long articles before consuming agent context.

Paper Search

Same two-step pattern over the academic paper corpus.

MCP Server setup

Connect Claude / Cursor / any harness in 60 seconds.

Getting Started

Connect

Personal Context

Knowledge Data

Crypto Prices

Prediction Markets

Equity Prices

ETF

Macro Indicators

SEC Filings

News

SEC Form 13F

What it does for your agent

Agent flow

Response

`wiki_search` response

`wiki_read` response

Notes

Direct invocation

Full parameter reference

Paper Search

MCP Server setup

​What it does for your agent

​Agent flow

​Response

​wiki_search response

​wiki_read response

​Notes

​Direct invocation

​Full parameter reference

​Related

Paper Search

MCP Server setup

What it does for your agent

Agent flow

Response

`wiki_search` response

`wiki_read` response

Notes

Direct invocation

Full parameter reference

Related