"A guide to technical report writing" matching MCP servers:

This MCP server enables AI agents to submit a single technical answer along with caller-supplied concepts, accepted synonyms, and numeric requirements for deterministic, rubric-based feedback.

Enables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.

Enables filling uploaded document templates (DOCX/HWP/HWPX/PDF) with chatbot-generated content while preserving original formatting, tables, and images, and saving as files.

Explainability Report - MCP server providing AI-powered tools and automation by MEOK AI Labs

MCP server for writing Markdown report fragments to files with path safety and SHA-256 verification.

Turns a local CSV or XLSX table into a full analysis report — summary statistics, duplicate and missing-value diagnostics, top category frequencies, significant pairwise correlations, a Markdown insights writeup, and an Excel workbook with native interactive charts — entirely offline with no uploads, API keys, or pandas dependency. It runs over stdio or streamable-http behind a free anonymous tier that returns an in-band quota signal rather than silently billing.

Content writing toolkit for headline generation, readability scoring, tone analysis, outline building, and plagiarism similarity checking.

An MCP server for Chinese financial reports, providing 44 tools to resolve companies, list/retrieve filings, extract report sections, run AI-based indicator extraction, search via Elasticsearch, and browse a 21,698-rule industry rules dashboard.

Provides deterministic parental guidance for children with chronic kidney disease, including food safety checks, lab report interpretation, and nutrient-aware food substitutions based on authoritative guidelines and the Chinese Food Composition Table.

A provably read-only SQL layer for AI agents (MCP) and weekly reports — over the database behind an ERP (Logo Tiger, Netsis, Mikro). Statement-checking guard, measured against 28 attacks.

An MCP server that transforms books into agent-callable skill packages, enabling AI agents to use books as guides for evidence, procedures, and tutoring.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

Searches Reddit for answers to questions and saves Q&A sessions to Notion.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.

Enables LLMs to provide voice status updates via OpenAI TTS, ideal for background task progress reports.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.