agent-lsp

The most complete MCP server for language intelligence. 65 tools, 30 CI-verified languages, 24 agent workflows. Single Go binary.

AI agents make incorrect code changes because they can't see the full picture: who calls this function, what breaks if I rename it, does the build still pass. Language servers have the answers, but existing MCP bridges either cold-start on every request or expose raw tools that agents use incorrectly.

agent-lsp is a stateful runtime over real language servers. It indexes your workspace once, keeps the index warm, and adds a skill layer that encodes correct multi-step operations so they actually complete.

What agents say

We asked AI agents to evaluate agent-lsp across 10 coding tasks (find callers, rename safely, preview edits, detect dead code) and write an honest assessment. Four different models, four independent evaluations, same conclusion:

Claude (Opus 4.6): "I would recommend agent-lsp for any workflow involving refactoring, impact analysis, or safe editing. The standout tools are blast_radius (blast radius in one call, with test/non-test partitioning that would take 5-10 grep commands to replicate), go_to_implementation (type-checked interface satisfaction that grep simply cannot do), and the simulation session workflow (speculative type-checking without touching disk, which has no grep/read equivalent at all)."

Cursor (auto): "I would recommend agent-lsp for heavy refactors and code navigation because the rename, references, implementations, call hierarchy, and simulation tools remove a lot of brittle grep/manual-edit work and make changes safer."

GPT-5.5 (via Codex): "I would recommend agent-lsp for symbol-aware work: references, implementations, rename previews, diagnostics, and large-file structure are materially faster and less error-prone than grep/read loops."

Gemini 2.5 Pro (via Gemini CLI): "I would highly recommend agent-lsp because it provides a level of semantic awareness that standard text-searching tools simply cannot match. The ability to perform high-confidence renames, find interface implementations, and preview the diagnostic impact of edits without writing to disk significantly reduces the risk of introducing regressions."

How the pieces fit together: LSP (Language Server Protocol) is how editors get code intelligence: completions, diagnostics, go-to-definition. MCP (Model Context Protocol) is the standard way AI tools like Claude Code discover and call external tools. agent-lsp bridges the two: language server intelligence, accessible to AI agents.

How it works

One agent-lsp process manages your language servers. Point your AI at ~/code/. It routes .go to gopls, .ts to typescript-language-server, .py to pyright. No reconfiguration when you switch projects. The session stays warm across files, packages, and repositories.

Tested, not assumed

Every other MCP-LSP implementation lists supported languages in a config file. None of them run the actual language server in CI to verify it works.

agent-lsp CI runs 30 real language servers against real fixture codebases on every push: Go, Python, TypeScript, Rust, Java, C, C++, C#, Ruby, PHP, Kotlin, Swift, Scala, Zig, Lua, Elixir, Gleam, Clojure, Dart, Terraform, Nix, Prisma, SQL, MongoDB, and more. When we say "works with gopls," that's a verified, automated claim, not a hope.

Speculative execution

Simulate changes in memory before writing to disk. No other MCP-LSP implementation has this.

preview_edit previews the diagnostic impact of any edit. You see exactly what breaks before the file is touched. simulate_chain evaluates a sequence of dependent edits (rename a function, update all callers, change the return type) and reports which step first introduces an error.

8 speculative execution tools. See docs/speculative-execution.md for the full workflow.

Token savings

Structured LSP responses use 5-34x fewer tokens than grep/read on the same tasks. On HashiCorp Consul (319K lines), a blast-radius analysis uses 17.7MB via grep vs 841KB via LSP, reducing 5,534 tool calls to 119. Savings scale with codebase size. See docs/token-savings.md for the full experiment across five codebases.

Persistent daemon mode

Python and TypeScript projects need minutes of background indexing before find_references works. agent-lsp automatically spawns a persistent daemon broker that survives between sessions, so the workspace stays indexed. First session: daemon starts and indexes (~10s for FastAPI). Subsequent sessions: instant connection to the warm daemon. Auto-exits after 30 minutes of inactivity. Go, Rust, and other fast-indexing languages bypass this entirely (zero overhead).

Phase enforcement

Skills tell agents the correct order of operations. Phase enforcement makes the runtime block violations instead of trusting the agent to follow instructions.

When an agent activates a skill, every tool call is checked against the current phase's permissions. Calling apply_edit during blast-radius analysis doesn't silently proceed; it returns an error with specific recovery guidance ("complete the blast_radius phase first, allowed tools: [blast_radius, find_references]"). Phases advance automatically as the agent calls tools from later phases.

No other MCP tool provider enforces workflow ordering at runtime. See docs/phase-enforcement.md.

Concurrency analysis

The inspector includes 4 concurrency checks that work across 25 languages in 4 concurrency families (goroutine, thread, async, actor):

• Unrecovered concurrent entry: goroutines/threads/tasks without recovery
• Unchecked shared state: bare type assertions on sync.Map, ConcurrentHashMap
• Channel never closed: channels/queues created but never closed (goroutine leaks)
• Shared field without sync: fields accessed from concurrent contexts without synchronization

blast_radius annotates symbols with sync_guarded: true when the parent type has a mutex. find_callers with cross_concurrent: true traces call chains through goroutine/thread boundaries. The /lsp-concurrency-audit skill produces a field-level safety report for any type.

Auto-diagnostics

Symbol edit tools (replace_symbol_body, insert_after_symbol, insert_before_symbol, safe_delete_symbol) automatically return errors_after and warnings_after counts. Agents know immediately whether an edit broke something without a separate get_diagnostics call.

safe_apply_edit combines preview + apply in one call: previews speculatively, applies to disk only if net_delta == 0 (no new errors). One tool call instead of three.

Works with

Skills

Raw tools get ignored. Skills get used. Each skill encodes the correct tool sequence so workflows actually happen without per-prompt orchestration instructions. Skills are available as AgentSkills slash commands and as MCP prompts via prompts/list / prompts/get for any MCP client.

See docs/skills.md for full descriptions and usage guidance.

Before you change anything

Editing safely

Getting started

Understanding unfamiliar code

After editing

Generating code

Full workflow

Docker

Stdio mode (MCP client spawns the container directly):

# Go
docker run --rm -i -v /your/project:/workspace ghcr.io/blackwell-systems/agent-lsp:go go:gopls

# TypeScript
docker run --rm -i -v /your/project:/workspace ghcr.io/blackwell-systems/agent-lsp:typescript typescript:typescript-language-server,--stdio

# Python
docker run --rm -i -v /your/project:/workspace ghcr.io/blackwell-systems/agent-lsp:python python:pyright-langserver,--stdio

HTTP mode (persistent service, remote clients connect over HTTP+SSE):

docker run --rm \
  -p 8080:8080 \
  -v /your/project:/workspace \
  -e AGENT_LSP_TOKEN=your-secret-token \
  ghcr.io/blackwell-systems/agent-lsp:go \
  --http --port 8080 go:gopls

Images run as a non-root user (uid 65532) by default. Set AGENT_LSP_TOKEN via environment variable, never --token on the command line. Images are also mirrored to Docker Hub (blackwellsystems/agent-lsp). See DOCKER.md for the full tag list, HTTP mode setup, and security hardening options.

Setup

Step 1: Install agent-lsp

curl -fsSL https://raw.githubusercontent.com/blackwell-systems/agent-lsp/main/install.sh | sh

brew install blackwell-systems/tap/agent-lsp

# PowerShell (no admin required)
iwr -useb https://raw.githubusercontent.com/blackwell-systems/agent-lsp/main/install.ps1 | iex

# Scoop
scoop bucket add blackwell-systems https://github.com/blackwell-systems/agent-lsp
scoop install blackwell-systems/agent-lsp

# Winget
winget install BlackwellSystems.agent-lsp

# pip
pip install agent-lsp

# npm
npm install -g @blackwell-systems/agent-lsp

# Go install
go install github.com/blackwell-systems/agent-lsp/cmd/agent-lsp@latest

Step 2: Install language servers

Install the servers for your stack. Common ones:

Full list of 30 supported languages in docs/language-support.md.

Step 3: Verify setup

agent-lsp doctor

Probes each configured language server and reports capabilities. Fix any failures before proceeding. See language support for install commands and server-specific notes.

Step 4: Configure your AI tool

agent-lsp init

Detects language servers on your PATH, asks which AI tool you use, writes the correct MCP config, and installs skill awareness rules for your AI provider (CLAUDE.md for Claude Code, .cursor/rules/ for Cursor, .clinerules for Cline, .windsurfrules for Windsurf, GEMINI.md for Gemini CLI). For CI or scripted use: agent-lsp init --non-interactive.

The generated config looks like:

{
  "mcpServers": {
    "lsp": {
      "type": "stdio",
      "command": "agent-lsp",
      "args": [
        "go:gopls",
        "typescript:typescript-language-server,--stdio",
        "python:pyright-langserver,--stdio"
      ]
    }
  }
}

Each arg is language:server-binary (comma-separate server args).

Step 5: Install skills

git clone https://github.com/blackwell-systems/agent-lsp.git /tmp/agent-lsp-skills
cd /tmp/agent-lsp-skills/skills && ./install.sh --copy

Skills are prompt files copied into your AI tool's configuration. --copy means the clone can be safely deleted afterward.

Skills are also available as MCP prompts: any MCP client can discover them via prompts/list and retrieve full workflow instructions via prompts/get, with no manual installation required. The install.sh path is for AgentSkills-compatible clients (Claude Code slash commands).

Step 6: Allow tool permissions (Claude Code)

For Claude Code, add mcp__lsp__* to your permissions allow list so all 65 tools are available without per-tool approval prompts:

// ~/.claude/settings.json
{
  "permissions": {
    "allow": ["mcp__lsp__*"]
  }
}

Without this, Claude Code will prompt for permission on each tool call. Other MCP clients handle permissions differently; check your client's documentation.

Skills are multi-tool workflows that encode reliable procedures: blast-radius check before edit, speculative preview before write, test run after change. See docs/skills.md for the full list.

Step 7: Start working

Your AI agent calls tools automatically. The first call initializes the workspace:

start_lsp(root_dir="/your/project")

This is what the agent does, not something you type. Then use any of the 65 tools. The session stays warm; no restart needed when switching files.

What's unique about agent-lsp

Use Cases

• Multi-project sessions: point your AI at ~/code/, work across any project without reconfiguring
• Polyglot development: Go backend + TypeScript frontend + Python scripts in one session
• Large monorepos: one server handles all languages, routes by file extension
• Code migration: refactor across repos with full cross-repo reference tracking
• CI pipelines: validate against real language server behavior
• Niche language stacks: Gleam, Elixir, Prisma, Zig, Clojure, Nix, Dart, Scala, MongoDB, all CI-verified

Multi-Language Support

30 languages, CI-verified end-to-end against real language servers on every CI run. No other MCP-LSP implementation tests a single language in CI.

Go, Python, TypeScript, Rust, Java, C, C++, C#, Ruby, PHP, Kotlin, Swift, Scala, Zig, Lua, Elixir, Gleam, Clojure, Dart, Terraform, Nix, Prisma, SQL, MongoDB, JavaScript, YAML, JSON, Dockerfile, CSS, HTML.

See docs/language-support.md for the full coverage matrix.

Tools

65 tools covering navigation, analysis, refactoring, symbol editing, composite exploration, safe editing, speculative execution, and session lifecycle. All CI-verified.

See docs/tools.md for the full reference with parameters and examples.

Further reading

Documentation

• Tools reference: full tool reference with parameters and examples
• Skills reference: skill reference, workflows, use cases, and composition
• Language support: language coverage matrix
• Architecture: system design and internals
• Speculative execution: simulate-before-apply workflows
• LSP conformance: LSP 3.17 spec coverage
• Docker: Docker tags, compose, and volume caching

Contributing

• CI notes: CI quirks and test harness details
• Distribution: install channels and release pipeline

Development

git clone https://github.com/blackwell-systems/agent-lsp.git
cd agent-lsp && go build ./...
go test ./...                   # unit tests
go test ./... -tags integration # integration tests (requires language servers)

Library Usage

The pkg/lsp, pkg/session, and pkg/types packages expose a stable Go API for using agent-lsp's LSP client directly without running the MCP server.

import "github.com/blackwell-systems/agent-lsp/pkg/lsp"

client := lsp.NewLSPClient("gopls", []string{})
client.Initialize(ctx, "/path/to/workspace")
defer client.Shutdown(ctx)

locs, err := client.GetDefinition(ctx, fileURI, lsp.Position{Line: 10, Character: 4})

See docs/architecture.md for the full package API.

License

MIT