"Information about 'Firma' and Its Context" matching MCP servers:

ocal-first CLI asset tracker for overseas stock investors. Tracks portfolio P\&L, net worth, and cash flow. Pulls earnings, insider trades, and SEC filings via Finnhub. 13 MCP tools for Claude Desktop — read and write your financial data in natural language.

Enables AI agents to search and read 1C:ITS portal subscription materials, such as development standards, platform documentation, and configuration guides, returning content as Markdown with source links.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Proxy for OpenAI-compatible backends that compacts conversation history before the context window fills, with read-only MCP tools to search and read back the archived originals.

Enables users to search locally across imported Claude, ChatGPT, and Claude Code conversations and retrieve surrounding turns with citations, while keeping indexing and storage on their own computer.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Provides official INDEC register designs and methodological rules to AI models, enabling accurate EPH data analysis code (R/Python) without hallucinations.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

Deterministic dependency + CVE context for AI coding tools, over the Model Context Protocol. A ~0.85 MB pure-Rust MCP server.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

Provides persistent personal context storage across AI conversations, allowing AI assistants to remember user preferences, project conventions, and other personal information between sessions.

Enables AI assistants to search and retrieve encrypted local screen history, including OCR text, recent activity, context around moments, daily rollups, and screen snapshots.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

Acts as a persistent memory and cross-tool shared context store, and builds a queryable codebase knowledge graph to slash token usage via structural answers.

Provides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.

Enables AI coding agents to consult a project's Git-versioned Markdown knowledge base over local stdio — checking knowledge health, listing the catalog, searching topics, and reading guides before acting. Access stays read-only, so project guidance, decisions, and lessons learned remain shared with human teammates and traceable beside the code.

Context Forge is a vendor-neutral continuity layer for AI tools. It stores project context in Markdown and JSON, retrieves task-scoped context, and leaves structured handoffs so another model can continue without being taught the project again.

Local-first memory for AI agents about the people in your life. MCP server + CLI on SQLite. Never phones home.

Enables MCP-capable clients to query coding-agent conversation exports and retrieve token-bounded evidence bundles with source provenance and hash verification, so LLMs can ground responses in verifiable history without replaying full transcripts.

Provides local, explicitly scoped memory for coding agents via MCP, storing durable project knowledge in a per-repository SQLite database with tools to record, search, and retrieve context.

Enables multiple AI coding agents and humans to coordinate through a Git-native Markdown blackboard (.context/ + AGENTS.md), where they can inspect workspace overviews, claim and toggle ready tasks, record architecture decisions, synthesize shared prompts, and hand off work via atomic, lock-protected writes that prevent lost updates and formatting drift. It also serves a fully offline local web dashboard that live-syncs any blackboard change over WebSocket.

Enables coding agents to run budgeted tests and checks under a countdown with a stop rule, shape long logs into concise failure summaries, and guard pre-existing test files against silent assertion rewrites. It also delivers relevance-filtered project memory and renders a token-and-time roadmap of the orchestrator and every subagent spawned for a task.

Enables LLM agents to compress handoffs into structured, auditable context capsules, preserving goals, constraints, decisions, and risks without external API calls.