"Gcore" matching MCP servers:

A remote MCP server exposing 3 tools via Model Context Protocol: score_prompt (free, grades any LLM prompt A-F on 0-40 scale), optimize_prompt (returns optimized prompt + dimension breakdown, $0.025 USDC), and compare_models (Claude vs GPT-4o head-to-head, $1.25 USDC). Remote HTTP server at /api/mcp.

Security analysis for Python codebases through the guard-core engine: SecurityConfig validation that catches keys pydantic silently drops, docs search over the vendored corpus, live detection probing, and verified setup guidance for Python, Typescript, Rust, PHP and Go adapters.

Scans websites to evaluate agent-readiness and produce an ASO Score Report across 34 signals, helping improve discoverability, trust, and interoperability for AI agents.

Enables AI to dynamically discover and invoke any Cin7 Core API endpoint using three generic tools (list, describe, invoke) plus a bug-report tool, covering the entire API surface without predefined endpoints.

MCP server that compresses LLM responses into caveman-style prose, reducing token usage by ~65% while maintaining technical accuracy.

Enables AI assistants to interact with DBT (Data Build Tool) projects, allowing them to query project metadata, inspect models and sources, view compiled SQL, and run DBT commands.

Classifies SAP ABAP objects against SAP's official Clean Core release catalog to determine API release status and guide S/4HANA implementation.

Shared, code-grounded memory for developers and their coding agents. Capture a learning once and the whole team plus every agent recalls it; memory is grounded in your code and stored as git-tracked JSON reviewed in PRs, with citations validated on write and stale memory withheld from recall. Works with any MCP client.

Enables AI agents to perform exact, reproducible mechanical computation — text diffing, hashing, JSON querying, calendar arithmetic, deadline-bounded regex extraction, CSV parsing, similarity scoring, unit and base conversion — instead of relying on probabilistic guessing. Runs dependency-free, read-only and offline over stdio, with schema validation and hard caps on every operation so no call can hang the client.

The Diagrams.so MCP server enables generating, editing, and managing cloud architecture diagrams from any MCP client, wrapping the public Diagrams.so API.

MCP server providing structured continuity memory for AI coding agents, tracking decisions, open loops, and session state in local SQLite. Enables agents to resume work from verified state across sessions without replaying transcripts.

Score your specs before an LLM builds from them. Scores on 4 axes (completeness, clarity, constraints, specificity), calculates a balance score, and returns a verdict with radar chart visualization.

MCP server that lets AI agents (Claude, Cursor) debug your .NET / ASP.NET Core app

Implicit is the experience layer for AI agents. Bounded local stdio MCP for synthetic experience addressing, selective state materialization, provenance, benchmarking, and adapter validation.

Enables scoring a local markdown documentation file out of 100 across categories like title, install, usage, license, badges, structure, and relative link resolution, with letter grades and a concrete list of fixes. It works on local files only, providing a heuristic checklist of documentation completeness rather than judging code quality, security, or SEO.

Enables AI agents and MCP clients to perform 3D asset creation and verification: exact b-rep CAD, GLB validation with embedded certificates, mesh inspection, and physics scenes, with most tools running without Blender.

Shared, versioned memory for your agents across Claude Code, Cursor, Kiro and Grok. One MCP server, sourced answers, full history.

Enables local-first specialist routing and evidence-based handoffs, including changelog management, continuity records, and allowlisted local Git snapshot and commit operations.

Provides semantic search and intelligent access to SAP AI Core documentation for AI assistants like Claude. It enables users to search across categories, retrieve full document content, and access topic-specific information from the SAP artificial intelligence repository.

Enables AI models to interact with IBM FileNet Content Manager for document management, folder operations, metadata handling, search, and content extraction through a standardized interface.