Veezee

Overview

Veezee is a data platform purpose-built for AI agents, offering structured access to LinkedIn, Reddit, and X (formerly Twitter) data through multiple integration surfaces. The product addresses a fundamental friction in agent development: social platforms actively block automated access, forcing developers to juggle separate APIs, rate limits, and authentication schemes. Veezee consolidates all three data sources behind a single credit meter, accessible via MCP (Model Context Protocol), REST API, TypeScript SDK, CLI, or agent skill packs. The platform is designed for agentic workflows—agents can mint their own free API key in a single HTTP call without human signup, making it uniquely suited for autonomous setups. Veezee positions itself as a developer-first alternative to expensive enterprise data vendors like Proxycurl and official platform APIs, with transparent per-call pricing starting at $0.002 per credit and a free tier of 200 credits per IP per day.

Key Features

Unified Multi-Platform Access – Veezee provides a single integration point for LinkedIn, Reddit, and X data. Instead of managing three separate API contracts, developers configure one MCP server endpoint (https://mcp.veezee.io/all) or one REST base URL, and all platform tools become available under the same authentication and billing scheme. This dramatically reduces integration complexity for agents that need cross-platform social intelligence.

Agent-Friendly Authentication – The platform eliminates human-in-the-loop signup for agents. A single POST /v1/keys/mint call returns a free API key, which the agent can store and reuse. If a request is made without a key, the error response includes instructions for automatic key recovery, enabling fully autonomous setup. This is a notable departure from traditional API services that require account creation, email verification, and payment method entry.

MCP-Native Architecture – Veezee is built around the Model Context Protocol, an emerging standard for connecting AI models to external tools. The MCP server exposes 17 tools across the three platforms, each with typed inputs and outputs. Agents using MCP-compatible hosts (Claude Code, Cursor, Windsurf, VS Code, Cline, Zed, n8n) can add Veezee with a single configuration line. The platform also supports streamable HTTP transport, making it compatible with remote agent deployments.

Transparent Credit Metering – Every API response includes the credit cost of the call. Failed calls are automatically refunded, and there are no per-row or per-record charges—only successful tool invocations consume credits. This pricing model aligns costs with actual value delivered, unlike many data APIs that charge per profile or per search result regardless of usefulness.

Comprehensive Tool Set – Veezee offers 17 distinct tools covering people search, profile enrichment, company lookup, post retrieval, discussion search, and URL resolution across all three platforms. LinkedIn tools include profile and company data, people search, and post retrieval. Reddit tools cover subreddit details, post and comment search, user lookup, and URL resolution. X tools provide profile data, tweet search, and tweet detail with full metrics. A usage tool (get_usage) allows agents to check remaining credits without cost.

Multiple Integration Surfaces – Beyond MCP, Veezee provides a REST API with OpenAPI specification, a TypeScript SDK with built-in retries and idempotency, a CLI tool (vz) that exposes every tool as a subcommand, and installable agent skill packs. This flexibility ensures compatibility with virtually any agent framework or development environment.

Self-Service Pricing – Veezee operates on a prepaid credit model with no subscription required. The free tier provides 200 credits per IP per day (approximately 6,000 per month). Paid packs start at $20.00 for 10,000 credits, with a monthly plan at $99.00 for 80,000 credits. Credits are added to the same key, so no reconfiguration is needed after purchase. The only human step in the entire product is the optional email confirmation that grants a one-time 10,000 credit bonus.

How It Works

Veezee's typical user journey begins with an agent or developer choosing an integration surface. For MCP-based agents, the setup involves adding a server URL to the agent's configuration file—for example, adding {"url": "https://mcp.veezee.io/all"} to Cursor's mcp.json. The agent then authenticates by opening a browser-based sign-in (email code, no password) or by minting a free API key via REST for headless environments.

Once connected, the agent can invoke any of the 17 tools using natural language or programmatic calls. For instance, an agent tasked with market research might call reddit_search with a query and time range, then use reddit_get_post to retrieve full discussion threads. Each response includes the credit cost, allowing the agent to track spending autonomously.

For developers building custom integrations, the REST API offers direct HTTP access. A typical workflow involves minting a key via POST /v1/keys/mint, storing the returned token, and then making authenticated requests to endpoints like /v1/linkedin/profiles or /v1/reddit/search. The TypeScript SDK abstracts these calls into typed methods with automatic retry logic.

The CLI tool (vz) provides a command-line interface for ad-hoc data retrieval. After running vz init to create and store a key, users can run commands like vz linkedin profile get williamhgates to fetch a LinkedIn profile directly from the terminal.

Use Cases

Candidate Sourcing – Recruiting agents can search LinkedIn for candidates matching specific criteria (job title, location, industry), retrieve full profiles with experience and education details, and then cross-reference those candidates' activity on Reddit or X for cultural fit assessment. Veezee's candidate sourcing use case outlines a complete workflow with estimated credit costs.

Prospect Enrichment – Sales development agents can take a list of company names or LinkedIn URLs, resolve them to structured profiles, and enrich them with recent posts or news mentions from X and Reddit. This provides a multi-dimensional view of a prospect's current priorities and pain points without manual research.

Brand Monitoring – Marketing agents can monitor Reddit discussions and X posts for brand mentions, sentiment trends, and competitor activity. The reddit_search tool with time range filtering enables weekly or monthly sweeps, while x_search captures real-time conversations. Results can be aggregated into summary reports with quotes and links.

Market Research – Product teams can use Veezee to gather competitive intelligence by searching Reddit for discussions about competing products, analyzing LinkedIn company pages for headcount and funding signals, and tracking X for industry influencer opinions. The platform's unified credit meter makes it economical to run multi-platform research at scale.

Company Research – Due diligence agents can retrieve LinkedIn company profiles, recent posts, and employee counts, then cross-reference with Reddit discussions about the company's products or culture. This provides a richer picture than any single data source alone.

Pricing & Value

Veezee's pricing is refreshingly straightforward. The free tier offers 200 credits per IP per day—enough for roughly 50 profile lookups or 30 Reddit searches. For heavier usage, prepaid packs start at $20.00 for 10,000 credits ($0.002 per credit), with a monthly subscription at $99.00 for 80,000 credits ($0.00124 per credit). Credits never expire and are added to the same API key, so no reconfiguration is needed after purchase.

Compared to alternatives, Veezee is significantly more affordable. The leading LinkedIn data vendor charges $1,999/month for volume plans, and the official X API requires an approved developer account and bills per record. Veezee's self-service model with no minimum commitment and automatic refunds for failed calls makes it accessible for small teams and individual developers. However, the free tier's IP-based limit may be restrictive for multi-user or server-side deployments, and the per-credit cost for small packs ($20 for 10,000 credits) is higher than the monthly plan's effective rate. For detailed cost comparisons, see the alternatives page.

Final Verdict

Veezee excels at what it sets out to do: provide a unified, agent-friendly data layer for three major social platforms. Its MCP-native design, autonomous key minting, and transparent pricing are genuinely innovative and address real pain points in agent development. The tool set is comprehensive, the documentation is thorough, and the integration surfaces cover virtually every use case.

Areas for improvement include the lack of a dedicated enterprise plan with guaranteed uptime SLAs, the absence of webhook-based real-time streaming, and the relatively small free tier for production workloads. Additionally, data freshness and completeness depend on the underlying platforms' APIs, which may introduce latency or gaps.

Overall, Veezee is a strong recommendation for developers building AI agents that need social data—especially those in recruiting, sales, marketing, and market research. Its low barrier to entry and pay-as-you-go pricing make it easy to evaluate and adopt. For teams already using MCP-compatible agent hosts, the setup is nearly instantaneous. Check the documentation for detailed tool references and client-specific guides.