Candidate MCP Server

Overview

The Candidate MCP Server is a lightweight, library‑centric Model Context Protocol service that equips large language models with structured access to candidate data. By exposing both resources (direct URLs and text payloads) and tools (actionable commands), it allows an AI assistant to retrieve, present, or even contact a job applicant without manual API calls. The design is intentionally modular: the server runs inside an existing application, and developers can supply candidate details at runtime—whether from a static JSON file, a remote resume endpoint, or a live database.

What Problem It Solves

Recruitment workflows often require AI agents to pull in résumé content, LinkedIn profiles, or personal websites and then act on that information—such as sending a follow‑up email. Traditional approaches involve multiple REST calls, manual parsing, and custom orchestration logic. The Candidate MCP Server consolidates these operations into a single, protocol‑driven interface that any MCP‑aware LLM can consume. This reduces boilerplate, eliminates the need for separate authentication layers per data source, and guarantees consistent naming conventions across tools and resources.

Core Value for Developers

For developers building AI‑powered hiring platforms, the server delivers a plug‑and‑play data layer. The candidate information is injected once during server creation, and every subsequent LLM interaction can reference it via a predictable URI scheme (). Because the server is not a standalone service, it integrates seamlessly into existing microservices or monoliths, allowing teams to control lifecycle, scaling, and security without exposing a public endpoint.

Key Features Explained

• Unified Resource Namespace – A set of deterministic URIs (, , etc.) that expose both textual and link‑based data. These can be used directly in prompts or retrieved through the tool set.
• Convenient Tool Set – Actions such as or provide one‑step access to data or external actions. The tool, for example, leverages Mailgun configuration to send emails on behalf of the AI.
• Transport Flexibility – The library offers prebuilt transports for standard input/output and HTTP/Express, but developers can plug in any custom transport that satisfies the MCP interface.
• Stateless Request Handling – When bound to an HTTP server, a new server instance is spawned per request, ensuring isolation and preventing session ID collisions in concurrent environments.

Real‑World Use Cases

• Automated Screening – An AI assistant can fetch a résumé and LinkedIn profile, summarize qualifications, and decide whether to proceed with an interview request.
• Candidate Outreach – Using the tool, a recruiter bot can send personalized follow‑up emails or interview invitations without manual email composition.
• Interview Preparation – The server can provide a candidate’s personal website content to help interviewers understand their projects and interests ahead of time.
• Compliance Auditing – By exposing all candidate data through a single protocol, audit logs can be centrally captured, ensuring that every access is traceable.

Unique Advantages

• Library‑First Design – Unlike monolithic services, the server is intended to be imported and instantiated within an existing codebase, giving developers full control over configuration, lifecycle, and deployment strategy.
• Protocol‑Level Abstraction – The MCP interface ensures that any LLM, regardless of vendor or version, can interact with the server using a consistent set of actions and resources.
• Extensibility – New candidate properties (e.g., portfolio links, certifications) can be added by extending the configuration object; no changes to the MCP schema are required.
• Secure Contact Flow – By delegating email sending to Mailgun, the server offloads SMTP credentials and spam‑filtering concerns, keeping sensitive data out of the LLM’s context.

In summary, the Candidate MCP Server transforms candidate information into a first‑class AI data source, enabling sophisticated recruitment workflows while keeping integration simple and secure.