MCP Server Development Services

MCP tools that give AI assistants structured, permission-scoped access to your databases, parameterised SQL queries with input validation to prevent injection, result formatting that returns clean structured data rather than raw rows, and explicit access controls per tool. Each query tool defines which tables it can read and what filters it accepts, so the AI never has access to tables outside its defined scope. Read replica routing for analytical queries that should not contend with production writes. Connection pooling via PgBouncer for high-frequency tool calls that would otherwise exhaust the database connection limit. For write tools (creating records, updating fields), every modification is logged with the timestamp, the AI session identifier, and the before/after values. Integration with PostgreSQL, MySQL, SQL Server, and MongoDB. The AI that can look up a customer record, check order history, and run a cohort analysis, and nothing else, without requiring your team to build a custom API endpoint for each question.

MCP tools wrapping your internal APIs and third-party business systems, translating natural language requests into authenticated, validated API calls your systems can act on. OAuth 2.0 token management built into the tool layer: tokens refreshed automatically before expiry so the AI never fails due to an expired credential. Retry logic with exponential backoff for transient API failures so a rate limit or timeout doesn't surface as an error to the AI. Response schema validation before returning data, if the API returns an unexpected structure, the tool surfaces a structured error rather than passing malformed data to the model. Common integrations we build: Salesforce SOQL queries and record creation, HubSpot deal and contact tools, Jira issue creation and status updates, Linear task management, Slack message sending, and your internal REST or GraphQL APIs. Each tool is tested against your actual API environment before deployment, not just mocked.

MCP tools for reading, listing, and searching your document repositories, local file systems, cloud object storage (S3, GCS, Azure Blob), SharePoint, Confluence, and knowledge base platforms. MCP resource URIs define the addressable scope of file access: a resource URI like s3://my-bucket/reports/ exposes only that prefix, not the full bucket, conforming to the MCP specification's resource model. Document extraction pipeline for PDFs (pdfminer or PyMuPDF for text extraction, preserving page and section structure for context), Word documents (python-docx), and plain text files. Content chunking strategy calibrated to your context window budget: semantic chunking that splits at paragraph or section boundaries rather than fixed character counts produces better-quality passages for the AI to reason over. The TypeScript MCP SDK (@modelcontextprotocol/sdk) is used to define resource handlers with typed schemas; resource handlers implement listResources() to enumerate available documents and readResource(uri) to return the content of a specific resource, both returning responses in the MCP specification's ResourceContents format. Tool definitions use JSON Schema with description fields that are read by the AI to determine when and how to call each tool, clear, specific descriptions are critical for the model to correctly select between a "search documents" tool and a "read specific document" tool without ambiguity. Semantic search via a vector index built from your document corpus: the AI retrieves the most relevant sections by embedding similarity rather than keyword match, then returns them with source attribution (filename, page number) so outputs are traceable. Access controls at the storage level: the tool only accesses the S3 prefix or SharePoint site it is granted, not the full storage account. Server lifecycle is managed via the MCP initialize/ping/shutdown protocol: the server declares its capabilities on initialization, responds to client capability negotiation, and handles graceful shutdown without leaving open file handles or dangling connections.

MCP tools that let AI take actions, creating records, updating fields, sending messages, triggering workflows, and completing multi-step operational tasks without requiring a human to click through each step. Built with appropriate guardrails at every level: confirmation step tools that return a preview of the proposed change for the AI to present to the user before executing; idempotency keys on write operations so retrying a failed tool call doesn't create duplicate records; rate limiting on high-volume tools that could generate many records in a short time; and an audit log for every action with the AI session identifier, the tool name, the input parameters, and the result. MCP tool definitions are declared in the server's listTools() handler using JSON Schema for input validation: each tool specifies its name, a description the AI uses for tool selection, and an inputSchema (JSON Schema object) that the MCP client validates before the call is dispatched. When a tool call arrives, the server's callTool() handler receives the validated parameters and returns a ToolResult containing either content (the action outcome) or an McpError with a structured error code, InvalidParams, ToolNotFound, or a custom application error. Rate limiting on high-volume tools is implemented at the server layer using a token bucket algorithm per client identity so a runaway agent loop cannot exhaust your backend API quota or create thousands of records before a human notices. Destructive actions (deletions, status changes, financial updates) require a separate confirmation tool call in the orchestration flow, the design enforces a review step rather than relying on prompt instructions. MCP servers are tested with MCP Inspector (the official Anthropic-provided debugging tool) before Claude Desktop integration: Inspector provides a visual tool call interface that lets you validate tool schemas, inspect request/response payloads, and confirm error handling without writing a full client. Deployment patterns: sidecar deployment co-located with your application server for low-latency tool calls; standalone service with OAuth 2.0 for multi-tenant or multi-client deployments where different users or teams connect to the same MCP server with individual permission scopes. SSE (Server-Sent Events) transport is used for HTTP-based MCP servers where the client maintains a persistent connection; stdio transport is used for Claude Desktop integrations where the MCP server runs as a local subprocess. Webhook trigger tools for connecting AI actions to downstream workflow systems (Zapier, Make, n8n, your internal event bus).

Security architecture designed to satisfy your compliance team, not just make the AI work. API key authentication with scoped keys: each key grants access to a defined set of tools, so a Claude Desktop integration key cannot access the same tools as an automated agent key. OAuth 2.0 PKCE flow for MCP clients that authenticate on behalf of a user, the AI acts with that user's permissions, not with a service account that has broad access. Transport security: TLS 1.3 only, certificate pinning for on-premises deployments, mutual TLS for high-sensitivity integrations. PII and sensitive field redaction before data is returned to the AI model: credit card numbers, national ID numbers, and passwords masked at the tool response layer regardless of what the database contains. Every tool request logged with timestamp, client identity, tool name, sanitised inputs, and response status, exportable for SOC 2 evidence and security audits. Rate limiting per client per tool to prevent runaway agent loops from degrading your backend systems.

Multi-step AI workflows that chain your MCP tools into end-to-end automated processes, not just single-turn queries but agents that complete a sequence of actions to achieve a defined outcome. LangGraph for stateful multi-step agents where the workflow has conditional branches and loops: an agent that checks a customer's tier, retrieves their order history, evaluates their eligibility for a refund against your refund policy, and either processes the refund or escalates to a human with the eligibility assessment already written, all triggered by a support ticket event. Context carryover across tool calls: agent state tracks what has been retrieved and what actions have been taken, so later steps build on earlier results rather than re-querying data already retrieved. Human-in-the-loop gates at defined points: high-value or irreversible actions pause for human confirmation before proceeding, then resume without re-running prior steps. Error handling for tool failures: the agent distinguishes between a retryable error (rate limit, transient timeout) and a hard failure requiring human intervention, and routes each accordingly.