GenuDo Market Intelligence MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| HOST | No | Bind address; Docker sets 0.0.0.0. | 127.0.0.1 locally |
| PORT | No | HTTP port. | 3000 |
| APIFY_TOKEN | No | For local live research. Calls source Actors and enables centralized Apify storage. Injected by Apify when hosted. | |
| MCP_BEARER_TOKEN | No | Optional application-level bearer authentication. | |
| MCP_ALLOWED_HOSTS | No | Comma-separated accepted HTTP hostnames. | |
| APIFY_META_ADS_ACTOR | No | Meta Ads source Actor. | apify/facebook-ads-scraper |
| ACTOR_WEB_SERVER_PORT | No | Apify container/Standby port; takes precedence over PORT. | typically 4321 |
| APIFY_RESEARCH_STORE_NAME | No | Named shared key-value store. | genudo-market-intelligence |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_meta_adsA | Search public Meta Ad Library data by keywords and market. This runs a paid Apify source Actor and stores normalized evidence for reuse. |
| research_ai_employee_marketA | Research one taxonomy category across EG/SA/UAE using its Arabic and English queries. Produces market metrics, evidence, and a clearly caveated opportunity-screening score. |
| analyze_competitor_adsA | Run a deep Meta Ads research pass for one competitor, including creative mix, CTAs, landing domains, localization, social metadata, and longest-running ads. |
| compare_ai_employee_opportunitiesA | Compare two or more AI employee categories using stored evidence. Does not start paid source runs; missing research is reported explicitly. |
| get_market_evidenceA | Retrieve normalized ads and advertisers from stored research, with optional run, market, category, and competitor filters. |
| get_research_runA | Retrieve the durable metadata, inputs, metrics, and summary for a previous research run. |
| list_sourcesA | Show connected and planned intelligence sources, centralized storage status, the AI employee taxonomy, and recent research runs. Never exposes secrets. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool targets a distinct part of the research workflow: keyword search, category research, competitor deep-dive, comparison, evidence retrieval, run metadata, and source listing. Clear descriptions and different action verbs prevent selection ambiguity.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., search_meta_ads, compare_ai_employee_opportunities, list_sources). No mixed conventions or vague verbs; the naming is predictable and readable.
With 7 tools, the server is well-scoped for market intelligence research. Each tool serves a clear purpose in the workflow—from raw search to analysis and retrieval—without redundancy or missing essentials.
The toolset covers the full research lifecycle: searching, researching, analyzing, comparing, and retrieving evidence/run metadata. Minor gaps like missing update/delete operations for runs or sources are not critical for a research-focused server.