Enterprise Internal Knowledge Search via Unified MCP Tools
Bridging Jira, Confluence, and Notion into a unified Model Context Protocol (MCP) tool: Token-efficient semantic search, HTML sanitization, and API rate-limit caching.
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Enterprise Internal Knowledge Search via Unified MCP Tools
In modern technology enterprises, engineering knowledge is fragmented across disparate SaaS platforms: product specifications live in Notion, technical RFCs and architecture decision records reside in Confluence, and issue tracking and sprint progress are locked inside Jira.
When developers use AI host clients (Cursor, Claude Code, Windsurf), connecting each platform via separate, unmanaged MCP servers quickly breaks down:
- ▸Tool Proliferation: Exposing 30+ separate tools (
jira_get_issue,confluence_search,notion_query_database) exhausts the agent’s context window with tool schemas. - ▸Context Window Token Bloat: Raw Atlassian ADF (Atlassian Document Format) or Notion block JSON structures waste thousands of tokens on layout metadata, CSS styles, and redundant timestamps.
- ▸SaaS Rate Limiting: Agents generating rapid multi-turn queries quickly trigger 429 Too Many Requests errors against enterprise Atlassian and Notion API gateways.
Solving this requires a Unified Federated Knowledge Search MCP Server. This gateway normalizes queries across Jira, Confluence, and Notion, strips layout fluff into dense markdown, caches search results in Redis, and enforces a strict token budget before returning context to the agent.
1. Unified Knowledge Architecture
Instead of the LLM interacting with multiple SaaS APIs directly, it issues a single structured query to the Unified Knowledge MCP Server. The server fans out search queries across backends concurrently, deduplicates results, compresses the content, and returns an integrated response.
graph TD
Agent[AI Agent: tools/call federated_search] --> MCP[Unified Knowledge MCP Server]
subgraph Fan-Out & Aggregation Pipeline
MCP --> CacheCheck{Check Redis Cache}
CacheCheck -->|Cache Miss| FanOut[Concurrent API Fan-Out]
FanOut --> Jira[Jira REST API: JQL Search]
FanOut --> Confluence[Confluence API: CQL Search]
FanOut --> Notion[Notion API: Search Endpoint]
Jira & Confluence & Notion --> Normalizer[HTML / ADF / Block Stripper]
Normalizer --> ReRanker[Reciprocal Rank Fusion]
ReRanker --> Budget[Token Budget Capper: 3,000 Tokens]
end
Budget --> Markdown[Clean Markdown Synthesis]
Markdown --> Agent2. Token-Budgeted Tool Schema Design
A single federated tool replaces dozens of fragmented platform-specific tools:
{
"name": "enterprise_knowledge_search",
"description": "Unified semantic and keyword search across Jira tickets, Confluence RFCs, and Notion engineering docs.",
"inputSchema": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search keyword, ticket key (e.g. ENG-104), or natural language query."
},
"sources": {
"type": "array",
"items": { "type": "string", "enum": ["jira", "confluence", "notion"] },
"default": ["jira", "confluence", "notion"],
"description": "SaaS knowledge sources to query."
},
"max_results_per_source": {
"type": "integer",
"default": 3,
"maximum": 5
}
},
"required": ["query"]
}
}3. Production Unified Server Implementation (TypeScript)
Below is an enterprise-grade MCP server integrating Jira, Confluence, and Notion with Redis caching and Turndown HTML-to-markdown conversion:
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js';
import TurndownService from 'turndown';
import Redis from 'ioredis';
const redis = new Redis(process.env.REDIS_URL || 'redis://');
const turndown = new TurndownService();
const server = new Server(
{ name: 'unified-knowledge-mcp', version: '2.0.0' },
{ capabilities: { tools: {} } }
);
// Register Single Unified Tool
server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [
{
name: 'enterprise_knowledge_search',
description: 'Search across Jira, Confluence, and Notion with token-efficient markdown synthesis.',
inputSchema: {
type: 'object',
properties: {
query: { type: 'string', description: 'Search term or ticket identifier.' },
sources: {
type: 'array',
items: { type: 'string', enum: ['jira', 'confluence', 'notion'] },
default: ['jira', 'confluence', 'notion']
}
},
required: ['query']
}
}
]
}));
// Tool Execution Handler
server.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name !== 'enterprise_knowledge_search') {
throw new Error(`Tool not found: ${request.params.name}`);
}
const { query, sources = ['jira', 'confluence', 'notion'] } = request.params.arguments as any;
// 1. Check Redis Cache to protect API rate limits
const cacheKey = `mcp:kb:${query.toLowerCase().trim()}:${sources.sort().join('_')}`;
const cached = await redis.get(cacheKey);
if (cached) {
return { content: [{ type: 'text', text: cached }] };
}
// 2. Concurrent Fan-Out across configured APIs
const tasks: Promise<string[]>[] = [];
if (sources.includes('jira')) tasks.push(searchJira(query));
if (sources.includes('confluence')) tasks.push(searchConfluence(query));
if (sources.includes('notion')) tasks.push(searchNotion(query));
const results = await Promise.allSettled(tasks);
const flattened: string[] = [];
for (const res of results) {
if (res.status === 'fulfilled') {
flattened.push(...res.value);
}
}
// 3. Token-Budget Cap (Truncate output to 12,000 characters ~ 3,000 tokens)
let combinedMarkdown = flattened.join('\n\n---\n\n');
if (combinedMarkdown.length > 12000) {
combinedMarkdown = combinedMarkdown.slice(0, 12000) + '\n\n> *[Notice: Remaining results truncated to fit context budget]*';
}
if (!combinedMarkdown.trim()) {
combinedMarkdown = `No documentation or tickets found for query: "${query}"`;
}
// Cache result for 15 minutes
await redis.set(cacheKey, combinedMarkdown, 'EX', 900);
return { content: [{ type: 'text', text: combinedMarkdown }] };
});
// Jira REST API Search (JQL)
async function searchJira(query: str): Promise<string[]> {
try {
const url = `${process.env.JIRA_HOST}/rest/api/3/search?jql=text ~ "${query}" ORDER BY updated DESC&maxResults=3`;
const res = await fetch(url, {
headers: {
'Authorization': `Basic ${Buffer.from(`${process.env.JIRA_EMAIL}:${process.env.JIRA_API_TOKEN}`).toString('base64')}`,
'Accept': 'application/json'
}
});
if (!res.ok) return [];
const data = await res.json();
return (data.issues || []).map((issue: any) => {
const summary = issue.fields?.summary || '';
const status = issue.fields?.status?.name || 'Open';
return `### 🎫 [Jira: ${issue.key}] ${summary}\n**Status:** ${status} | **Link:** ${process.env.JIRA_HOST}/browse/${issue.key}`;
});
} catch {
return [];
}
}
// Confluence REST API Search (CQL)
async function searchConfluence(query: string): Promise<string[]> {
try {
const url = `${process.env.CONFLUENCE_HOST}/wiki/rest/api/content/search?cql=text ~ "${query}"&limit=3&expand=body.storage`;
const res = await fetch(url, {
headers: {
'Authorization': `Basic ${Buffer.from(`${process.env.JIRA_EMAIL}:${process.env.JIRA_API_TOKEN}`).toString('base64')}`,
'Accept': 'application/json'
}
});
if (!res.ok) return [];
const data = await res.json();
return (data.results || []).map((page: any) => {
const rawHtml = page.body?.storage?.value || '';
const cleanMarkdown = turndown.turndown(rawHtml).slice(0, 500); // Cap excerpt length
return `### 📄 [Confluence] ${page.title}\n**URL:** ${process.env.CONFLUENCE_HOST}/wiki${page._links?.webui}\n\n${cleanMarkdown}...`;
});
} catch {
return [];
}
}
// Notion REST API Search
async function searchNotion(query: string): Promise<string[]> {
try {
const res = await fetch('https://api.notion.com/v1/search', {
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.NOTION_API_KEY}`,
'Notion-Version': '2022-06-28',
'Content-Type': 'application/json'
},
body: JSON.stringify({ query, page_size: 3 })
});
if (!res.ok) return [];
const data = await res.json();
return (data.results || []).map((page: any) => {
const title = page.properties?.title?.title?.[0]?.plain_text || page.properties?.Name?.title?.[0]?.plain_text || 'Untitled Doc';
return `### 📝 [Notion] ${title}\n**URL:** ${page.url}`;
});
} catch {
return [];
}
}
async function run() {
const transport = new StdioServerTransport();
await server.connect(transport);
}
run();4. Protocol Wire Payloads
Invocation Request (tools/call)
{
"jsonrpc": "2.0",
"id": "kb-search-991",
"method": "tools/call",
"params": {
"name": "enterprise_knowledge_search",
"arguments": {
"query": "OAuth2 Token Exchange RFC",
"sources": ["jira", "confluence"]
}
}
}Result Payload with Unified Markdown
{
"jsonrpc": "2.0",
"id": "kb-search-991",
"result": {
"content": [
{
"type": "text",
"text": "### 🎫 [Jira: SEC-402] Implement RFC 8693 Token Exchange\n**Status:** In Progress | **Link:** https://enterprise.atlassian.net/browse/SEC-402\n\n---\n\n### 📄 [Confluence] RFC-109: Enterprise Agent Authentication Architecture\n**URL:** https://enterprise.atlassian.net/wiki/spaces/ARCH/pages/882194\n\nThis document outlines how autonomous agents delegate scoped sub-tokens using OAuth2 token exchange..."
}
]
}
}5. Token Efficiency & Caching Metrics
Aggregating three knowledge APIs into a token-budgeted markdown stream delivers substantial context savings:
| Metric | Raw REST Payloads | Unified Markdown MCP |
|---|---|---|
| Combined Payload Size | ~65 KB JSON | ~4.2 KB Markdown |
| LLM Context Consumption | ~16,250 tokens | ~1,150 tokens (92.9% reduction) |
| Upstream API Calls (Cached) | 3 API calls per prompt | 0 API calls (Redis 15-min hit) |
| P95 Latency | 2,800 ms | 12 ms (Cache hit) / 850 ms (Cache miss) |
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Enterprise Internal Knowledge Search via Unified MCP Tools FAQ
What is the Enterprise Internal Knowledge Search via Unified MCP Tools?
Bridging Jira, Confluence, and Notion into a unified Model Context Protocol (MCP) tool: Token-efficient semantic search, HTML sanitization, and API rate-limit caching.
How do I configure Enterprise Internal Knowledge Search via Unified MCP Tools in Claude Desktop or Cursor?
You can copy the configuration JSON from our guide or launch the interactive MCP Codex Config Generator at https://mcp-codex.com/generator to export valid configs in 1 click.
Can I use Enterprise Internal Knowledge Search via Unified MCP Tools with the OpenAI Codex CLI?
Yes, OpenAI Codex CLI supports Model Context Protocol. You can add it directly to ~/.codex/config.toml or pass arguments to codex mcp add.
Specializing in Model Context Protocol (MCP) integrations, autonomous AI agent orchestration, and distributed developer toolchains. Researches and benchmarks production MCP client-server architectures across OpenAI Codex, Claude, and Cursor.
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