"Document analysis tools for legal documents and evidence" matching MCP servers:

Enables AI coding agents to assemble tamper-evident, locally stored evidence bundles that capture Git state, command results, and file hashes, then verify freshness and export shareable reports.

Enables coding agents to select bounded Python source context, run configured checks, and retrieve compact failure reports, diagnostic comparisons, and freshness evidence tied to the checked code.

Enables AI agents to run evidence QC gates mid-workflow—checking claims, sources, prose, and vault hygiene deterministically before research ships.

An MCP server that makes backend API requests and packages request+response pairs as evidence to prove an endpoint works.

Local MCP consistency checks for supplied OAuth test results and release evidence. Two separately launched Python stdio servers; checks test supplied-value consistency, not authenticity, live proof, or approval. Weio is operated by AI agents under human ownership.

Enables an assistant to verify whether claims about AI systems hold against supplied evidence bytes, replay conformance vectors, and inspect related terms and artifact digests offline without fetching, writing, or sending data.

A read-only Model Context Protocol server that converts local Git changes and JUnit XML reports into traceable release evidence, risk findings, and a rollback checklist.

Enables AI coding agents to test C++ code by running static analysis, sanitizers (ASan, TSan, LSan, UBSan), and performance profiling through eight MCP tools.

Provides comprehensive code analysis through three MCP servers: static analysis for code quality and security, dependency analysis for package management and vulnerabilities, and complexity analysis for maintainability assessment across multiple programming languages.

A local MCP server that provides Sonar-grade static analysis (bugs, vulnerabilities, code smells) for TypeScript/JS and C# repositories, with CLI and dashboard support.

Enables MCP clients to check acceptance evidence against a specification and JUnit results, and to review structured AI proposals for current contract hash, coverage, and in-scope paths. Both tools are read-only, idempotent, content-based, and require no model credentials or filesystem access.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

Enables generating JSON Schema from sample data, generating TypeScript types, validating data against schemas, creating mock data from schemas, and comparing schemas for differences.

Webhook management and testing tools for AI agents. Provides tools for sending, validating, generating, and debugging webhooks.

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables validating JSON/JSONC, getting/setting values with jq-like paths, pretty-printing or minifying, validating against Ajv JSON Schema, and diffing local files or inline strings without network calls.

Enables validating IFC models against IDS requirements with coverage reporting, auditing LOD by project stage, and extracting quantity take-offs, including combined audit summaries and full BIM health checks. It exposes these capabilities as MCP tools for Claude Desktop, producing Excel, HTML, and BCF reports.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Enables AI agents to run configured test suites, inspect failures and logs, compare runs, and draft or file bug reports only after human confirmation, with safeguards such as selector validation, environment isolation, timeouts, and redaction.

Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.

MCP server providing code review, auto-fix, test running, linting, formatting, and refactor suggestions for Claude Code.

Enables AI agents to interact with Ethereum smart contracts through Anvil, providing capabilities to read Solidity code, simulate and execute transactions, manipulate blockchain state, query events, and test contracts in isolated environments for development and auditing workflows.

Provides 32 remote tools that let agents validate common data formats, optimize or minify code and content, and generate synthetic test data such as fake records, IDs, and passwords.

Enables minting canary markers into package-generated synthetic datasets and scanning text you control to detect when a marker released to one recipient reappears elsewhere. Produces a payload-free, hash-linked evidence chain whose export verifies offline.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Code linting and style checking tools for AI agents, exposed as an MCP server. Supports style checks, naming conventions, complexity analysis, dead code detection, and import analysis.

Bridges AI agents with VS Code's debugger, enabling breakpoint management, step execution, variable inspection, and stack tracing via CLI commands.

Provides AI agents with web accessibility analysis tools via MCP, enabling checks for alt text, heading hierarchy, color contrast, ARIA validation, and form accessibility.

Enables AI agents and test runners to control Android devices via local ADB, including listing devices, tapping, swiping, typing, sending system keys, launching apps, and dumping UI hierarchy. Communicates over MCP stdio without exposing network listeners.