Documentation Index
Fetch the complete documentation index at: https://docs.wokelo.ai/llms.txt
Use this file to discover all available pages before exploring further.
Why use Wokelo MCP?
You are likely already using an AI assistant — Claude, ChatGPT, Copilot, or similar. But every one of them shares the same core limitations: training data with a cutoff date, no access to premium sources, a reliance on web search that surfaces the same SEO-optimized headlines everyone else sees, and the need for constant back-and-forth prompting just to get to a usable result. Wokelo MCP is built to solve every one of those limitations for dealmaking research — bundled premium subscriptions, a native company and transactions database, 0 hallucination, a proprietary news algorithm to surface signals your competitors aren’t seeing, and a single query that delivers a finished deliverable without you staying in the loop.| Generic AI assistant | Wokelo MCP | Relevant for | |
|---|---|---|---|
| Company data | Open web + training data with a cutoff date; misses emerging or less-covered players | 100M+ entity-resolved company database queried directly | Company discovery & enrichment |
| Premium sources | Inaccessible — requires separate subscriptions | 30+ pre-integrated premium sources, bundled | Company research |
| Transaction data | No native deal database | Exhaustive fundraises + M&A database; every acquirer/target entity-resolved | Deal & M&A research |
| Signal quality | Web search returns SEO-optimized headlines — the same results everyone else sees | Proprietary news algorithm surfaces hidden signals not biased by headlines | Company discovery, research, monitoring & alerts |
| Source integrity | Source credibility unpredictable; no deduplication or entity resolution | Thousands of verified global publishers; deduplicated, entity-resolved | Monitoring & alerts |
| Accuracy | 10–40% hallucination rate on structured queries; compounds with list length | Zero hallucination — every data point source-verified | End-to-end dealmaking research |
| Bulk enrichment | Manual re-prompting every 20–25 rows; credits exhausted across sequential calls | Full list enriched in a single MCP call | Company enrichment |
| Output format | Raw links or text requiring human synthesis; never client-ready | Structured briefing or finished PDF/PPT in your firm’s template | Research deliverables |
| Methodology | Cannot codify or consistently apply firm-specific frameworks | Firm methodology codified once; applied consistently on every run | Research deliverables |
| Workflow | Requires active involvement throughout | Trigger and pick up the finished deliverable asynchronously | Research deliverables |
What’s in the Bundle?
Wokelo MCP aggregates 30+ premium data sources so you don’t have to manage separate subscriptions or stitch together sources yourself. Coverage spans every dimension relevant to dealmaking research.- Firmographics
- Financials
- Regulatory filings
- Transactions & deals
- Web & app traffic
- News & media
- Business reviews
- Hiring & job postings
- Employee reviews
- Software reviews
- Patents & IP
- Social media
- Podcasts & interviews
- Academic & scientific publications
Use Case 1: Company Enrichment
Sample Query I have a management meeting with Notion on Thursday. Can you get me up to speed on their business, funding history, how they’re growing, and any red flags in the team or culture? Also help me figure out what questions I should be walking in with. What your LLM chat assistant does- Interprets your query, determines what company data is required, and pulls it directly from Wokelo
- You can ask follow-up questions and drill into specific areas within the same conversation.
- Sources and aggregates verified data from its proprietary database of 100M+ companies and 30+ premium sources.
- Responses are grounded in verified intelligence which are not accessible via the open web or model training data.
Use Case 2: Company Identification and Bulk Enrichment
Sample Query Find the top LLM observability startups in the US who have raised funding in the last 3 years and enrich each with funding stage, amount raised, key investors, and key products / services offered. Output an excel file. What your LLM chat assistant does- Interprets your query and calls Wokelo with the right parameters
- Receives the enriched list from Wokelo and presents it in a structured, ready-to-use excel file
- Lets you filter, refine, and ask follow-up questions within the same conversation
- Applies filters to identify the relevant company universe from its proprietary database of 100M+ companies.
- Sources every data point independently from its database, premium datasets, and open web
- Returns a fully enriched, structured list in a single pass with field level accuracy.
Use Case 3: Transaction Comparables in a Sector
Sample Query Pull M&A transactions in US outpatient behavioral health from the last 3 years including acquirers, targets, deal values and products / services offered. What patterns are you seeing in terms of business models attracting capital? What your LLM chat assistant does- Interprets the query and calls Wokelo with the right sector and date parameters
- Identifies emerging business models and themes based on recent funding and deal activity in Wokelo’s output
- Presents the deal table in a structured, ready-to-use format and lets you iterate within the same conversation
- Queries its exhaustive transaction database, unaffected by recency bias, to identify relevant deals
- Enriches each record with acquirer, target, financial metrics, and key products and services
- Returns a structured deal table ready for analysis
Use Case 4: Portfolio Company and Peer Sentiment Analysis
Sample Query We have a value creation review for Make.com next week. Can you pull employee review and software review data for them and their three closest peers, compare culture scores and leadership ratings, and flag any themes worth paying attention to? What your LLM chat assistant does- Interprets the query, identifies the peer set, and calls Wokelo with the right parameters
- Synthesizes the output to surface common themes, outliers, and red flags across the peer group
- Lets you drill into specific companies or sentiment dimensions within the same conversation
- Pulls employee sentiment from Glassdoor and software review data from G2 — alt data sources a generic LLM cannot reliably access or structure
- Returns culture scores, leadership ratings, and product perception signals across the defined company set
- Delivers a clean, side-by-side comparison table ready for analysis
Use Case 5: Scheduled Portfolio Company News Brief
Sample Query Every Monday morning, send me a digest of key developments across our portfolio companies from the prior week. What your LLM chat assistant does- Runs the query on a configured schedule and delivers the digest to your preferred channel automatically (scheduled query / Cowork)
- Synthesizes Wokelo’s output into a concise weekly digest
- Flags the most significant developments across the portfolio
- Monitors thousands of credible global publishers in real time
- Returns deduplicated, entity-resolved company intelligence — cleaned and structured on each run
Use Case 6: Trigger a Custom Research Workflow
Sample Query Run our Proposal Deck Creation workflow and build a proposal deck on construction tech vertical SaaS. What your LLM chat assistant does- Interprets the query and triggers the right Wokelo workflow
- Notifies you when the deliverable is ready
- Triggers the firm’s pre-configured research workflow built in Wokelo’s no-code agent builder
- Runs asynchronously in the background — every section, header, chart, and visual populated according to the firm’s defined methodology
- Delivers a finished, branded PDF and PowerPoint in the firm’s template — consistent every time, regardless of who runs it