MCP Connector Featured

Connect AI Agents to EVC Mesh

Official MCP server for EVC Mesh, connecting AI agent clients to task management, persistent memory, and multi-agent coordination via MCP tools.

Works with claude desktopcursorvscodeclaude code

91
Spark score
out of 100
Status Verified Official
Updated last month
Version 1.0.0
Models
claudegpt 4

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Why it matters

Integrate your AI agents with the EVC Mesh for seamless multi-agent coordination. This connector facilitates task management, event publishing, and artifact uploads.

Outcomes

What it gets done

01

Connect AI agents to Mesh via MCP tools

02

Manage tasks across multiple agents

03

Publish events and upload artifacts

04

Share persistent memory and recall across sessions

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/evc-mesh-mcp | bash

Capabilities

Tools your agent gets

heartbeat

Register agent presence and keep the session alive

get_my_tasks

List all tasks currently assigned to the calling agent

get_task

Fetch full task details including comments, artifacts, and metadata

list_tasks

Query tasks in a project by status, priority, label, or assignee

create_task

Create a new task with title, description, labels, assignee, and due date

create_subtask

Create a child task under a parent task with optional dependency edge

move_task

Transition task status between todo, in_progress, review, and done

update_task

Patch task fields such as priority, labels, due date, and title

+12 tools

Overview

EVC Mesh MCP

EVC Mesh MCP is the official MCP server connecting AI agent clients to EVC Mesh for task management, persistent memory, event publishing, and multi-agent coordination, with core (20-tool) and full (45-tool) profiles and stdio or SSE transport. Use it to connect an MCP-compatible agent to a running EVC Mesh instance; choose core for small-context models, full for automation or admin work, and SSE when multiple agents share one endpoint.

What it does

EVC Mesh MCP is the official Model Context Protocol server for EVC Mesh, a task-management platform for coordinating humans and AI agents. It connects AI agents (Claude Code, Cursor, Cline, OpenClaw, and others) to Mesh via MCP tools covering task management, persistent memory, event publishing, and multi-agent coordination. It ships two tool profiles to control context-window overhead: core (20 tools, about 6K tokens or 3% of a 200K context, aimed at Claude Code, Cursor, and other small-context models) and full (45 tools, about 14K tokens or 7% of 200K, aimed at power users, automation agents, and admin operations), selected via the MESH_MCP_PROFILE environment variable, default full. The core profile covers Agent Context Protocol and identity tools (heartbeat, get_project_knowledge, get_my_rules, get_context, get_my_tasks), task management (list_projects, list_tasks, get_task, create_task, update_task, move_task, assign_task, get_task_context), communication (add_comment, publish_event), memory (recall, remember, forget), and utility (report_error, session_report). The full profile adds 25 more across additional task tools (get_project, create_subtask, add_dependency, checkout_task, release_task), comments and artifacts (list_comments, upload_artifact, list_artifacts, get_artifact), the event bus (publish_summary, subscribe_events, poll_tasks), agent and team management (register_sub_agent, list_sub_agents, get_team_directory, update_agent_profile), governance and config (get_project_rules, get_assignment_rules, get_workflow_rules, import_workspace_config, export_workspace_config), and recurring tasks (create_recurring_task, list_recurring_schedules, get_recurring_history, trigger_recurring_now). Its recall tool has documented, specific guarantees: limit is a hard bound on results returned, rows failing scope or tags filters are dropped rather than returned unmarked regardless of how they were sourced, knowledge-graph-expanded neighbours (when enabled) are marked with graph_boost: true and provenance: via:graph and capped at at most limit/4 of the page in its tail slots rather than appended on top, and query-classification presets only fill in parameters you didn't supply - an explicit limit always wins. The server defines an Agent Context Protocol: a five-step session-start sequence (heartbeat online, get_project_knowledge, get_my_rules, get_context, get_my_tasks) and a session-end pair (publish_event with a persisted summary, then session_report with model and token metrics). Architecturally it's a lightweight proxy - AI agent to MCP server, stdio or SSE, to the Mesh REST API to PostgreSQL, Redis, NATS, and S3 - with no direct database access of its own.

When to use - and when NOT to

Use it to connect any MCP-compatible AI agent client to an existing EVC Mesh instance for task management, memory, and multi-agent coordination. Choose the core profile for small-context models or when minimizing context overhead matters (Claude Code, Cursor); choose full for power users, automation agents, or admin operations needing governance, artifacts, recurring tasks, or team management. Use SSE transport specifically when multiple agents need to share one MCP endpoint - it serves both profiles simultaneously on separate paths (/sse plus /message for full, /core/sse plus /core/message for core), authenticated per connection via a Bearer header, an X-Agent-Key header, or an agent_key query parameter. It requires a running EVC Mesh instance and an agent already registered in Mesh with an API key - it is a client and proxy layer, not a replacement for the Mesh platform itself, and it has no CD pipeline, so production deploys are manual.

Capabilities

Beyond the tool tables above, the server enforces a strict deploy order for its own production updates: database migrations (goose up) must succeed before the binary is swapped, and the binary before the service restarts - swapping before migrations pass is explicitly disallowed. It does not run its own migrations; it relies on the Mesh API's schema staying in sync.

How to install

Requires Go 1.22+, a running EVC Mesh instance, and an agent registered in Mesh with an API key (agk_...). Install via go install github.com/entire-vc/evc-mesh-mcp@latest, or clone and go build -o evc-mesh-mcp . from source. Configure via environment variables: MESH_API_URL (required, defaults to http://localhost:8005), MESH_AGENT_KEY (required for stdio mode), MESH_MCP_PROFILE (core or full, default full), MESH_MCP_TRANSPORT (stdio or sse, default stdio), and MESH_MCP_HOST/MESH_MCP_PORT for SSE mode (defaults 0.0.0.0/8081). For Claude Code, add an evc-mesh entry to .mcp.json with the command and env vars; for Cursor, add the equivalent entry under Settings > MCP Servers. Its production migration step, quoted verbatim:

DB_URL=$(grep ^DATABASE_URL /opt/evc-mesh/.env.prod | cut -d= -f2-)
  docker run --rm --network host \
    -v /opt/evc-mesh/migrations:/migrations \
    ghcr.io/pressly/goose:latest \
    goose -dir /migrations postgres "$DB_URL" up

Licensed MIT.

Who it's for

Teams running EVC Mesh who want their AI agent clients - Claude Code, Cursor, Cline, OpenClaw, or any other MCP-compatible client - to manage tasks, persistent memory, and multi-agent coordination directly through MCP, with a context-conscious core profile for lightweight agents and a full profile for automation and admin work.

Source README

EVC Mesh MCP Server

Install via Spark

Model Context Protocol (MCP) server for EVC Mesh - a task management platform for coordinating humans and AI agents.

Connects AI agents (Claude Code, Cursor, Cline, OpenClaw, etc.) to EVC Mesh via MCP tools for task management, persistent memory, event publishing, and multi-agent coordination.

Prerequisites

  • Go 1.22+
  • Running EVC Mesh instance
  • Agent registered in Mesh with an API key (agk_...)

Installation

go install github.com/entire-vc/evc-mesh-mcp@latest

Or build from source:

git clone https://github.com/entire-vc/evc-mesh-mcp.git
cd evc-mesh-mcp
go build -o evc-mesh-mcp .

Tool Profiles

The MCP server supports two profiles to optimize context window usage:

Profile Tools Context overhead Best for
core 20 ~6K tokens (3% of 200K) Claude Code, Cursor, small-context models
full 45 ~14K tokens (7% of 200K) Power users, automation agents, admin ops

Set via MESH_MCP_PROFILE environment variable. Default: full.

Configuration

Variable Required Default Description
MESH_API_URL Yes http://localhost:8005 Base URL of the Mesh API
MESH_AGENT_KEY Yes (stdio) - Agent API key (agk_...)
MESH_MCP_PROFILE No full Tool profile: core or full
MESH_MCP_TRANSPORT No stdio Transport mode: stdio or sse
MESH_MCP_HOST No 0.0.0.0 SSE server bind host
MESH_MCP_PORT No 8081 SSE server bind port

Claude Code (stdio mode)

Add to your project's .mcp.json:

{
  "mcpServers": {
    "evc-mesh": {
      "command": "evc-mesh-mcp",
      "env": {
        "MESH_API_URL": "https://your-mesh-instance.example.com",
        "MESH_AGENT_KEY": "agk_your-workspace_your-key",
        "MESH_MCP_PROFILE": "core"
      }
    }
  }
}

Cursor

Add to Cursor MCP settings (Settings → MCP Servers):

{
  "evc-mesh": {
    "command": "evc-mesh-mcp",
    "env": {
      "MESH_API_URL": "https://your-mesh-instance.example.com",
      "MESH_AGENT_KEY": "agk_your-workspace_your-key",
      "MESH_MCP_PROFILE": "core"
    }
  }
}

SSE Mode (multi-agent, shared server)

For connecting multiple agents through a shared MCP endpoint:

MESH_API_URL=https://your-mesh-instance.example.com \
MESH_MCP_PORT=8081 \
evc-mesh-mcp --transport sse

SSE mode serves two profiles simultaneously on different paths:

Path Profile Description
/sse + /message full All 45 tools (backward compatible)
/core/sse + /core/message core 20 essential tools

Authentication per connection via:

  • Authorization: Bearer agk_... header
  • X-Agent-Key: agk_... header
  • ?agent_key=agk_... query parameter

Agent Context Protocol (ACP)

At session start, follow these 5 steps in order:

1. heartbeat(status="online")              → register as alive
2. get_project_knowledge(project_id)       → load accumulated decisions & conventions
3. get_my_rules(project_id)                → understand constraints
4. get_context(project_id)                 → see recent activity + project knowledge
5. get_my_tasks()                          → check assigned work

At session end:

publish_event(type="summary", memory={persist: true})  → broadcast + persist
session_report(model, tokens_in, tokens_out)           → report metrics

MCP Tools - Core Profile (20)

ACP & Identity

Tool Description
heartbeat Send heartbeat. Call at session start with status=online
get_project_knowledge Get ALL permanent knowledge (decisions, conventions). ACP Step 2
get_my_rules Get ALL governance rules (workflow + assignment). ACP Step 3
get_context Get recent activity + project knowledge. ACP Step 4
get_my_tasks Get assigned tasks. ACP Step 5

Task Management

Tool Description
list_projects List workspace projects
list_tasks List tasks with filters (status, priority, assignee, search)
get_task Get task details with optional comments/artifacts/deps
create_task Create a new task
update_task Update task fields
move_task Change task status using slugs
assign_task Assign/unassign a task
get_task_context Get everything about a task in one call

Communication

Tool Description
add_comment Add comment to a task (markdown)
publish_event Publish event + optional memory hint for persistence

Memory

Tool Description
recall Search memory by keywords
remember Save knowledge (UPSERT by key)
forget Delete a memory entry
What recall guarantees about its result

limit is a hard bound. The response never contains more than limit items,
and total always equals the number of items actually returned. Nothing is added
to the page after it has been sized - not pinned rows, not graph-expanded
neighbours.

Rows that fail scope/tags/tags_any are dropped, never returned unmarked.
This holds regardless of how a row reached the result: ordinary retrieval, pinning,
or graph expansion. A pinned row is exempt from ranking, not from eligibility -
"pinned" means "do not let ranking bury this", not "show this to a caller who asked
for a different scope".

Graph neighbours are marked and bounded. With RECALL_GRAPH_ENABLED=true,
recall also runs a knowledge-graph expansion and folds in hop > 0 neighbours,
each carrying graph_boost: true and provenance: via:graph. They occupy at most
limit/4 of the page (at least 1 when limit >= 2, none when limit < 2) and take
its tail slots, displacing the weakest retrieval hits rather than being appended
on top. When expansion returns nothing usable, the page is exactly the base result -
the reserve is a ceiling, not a quota. graph_boost_count reports how many slots
were actually spent.

The reserve exists because base hits carry score (RRF across the retrieval arms)
and neighbours carry composite_score from a separate traversal - different fields
on different scales. Sorting the union on a common key does not balance them; in
practice every observed neighbour ranks below every base hit, so a naive merge-sort
would silently disable graph boost. The reserve makes that trade explicit and
tunable.

Presets never overrule you. recall classifies the query and may apply a
profile (e.g. multi-session widens the page). A profile only fills in parameters you
did not supply; an explicit limit always wins.

Utility

Tool Description
report_error Report an error on a task
session_report Report session metrics (model, tokens, cost)

MCP Tools - Full Profile (adds 25 more)

Additional Task Tools

Tool Description
get_project Get project details with statuses and custom fields
create_subtask Create subtask under a parent (status_slug optional; defaults to the project's default status, not the parent's)
add_dependency Add dependency between tasks
checkout_task Atomic task lock for multi-agent coordination
release_task Release atomic task lock

Comments & Artifacts

Tool Description
list_comments List task comments
upload_artifact Upload file/code/log to a task
list_artifacts List task artifacts
get_artifact Get artifact details and download URL

Event Bus

Tool Description
publish_summary Publish work summary (convenience wrapper)
subscribe_events Configure webhook delivery for events
poll_tasks Long-poll for new task assignments

Agent & Team

Tool Description
register_sub_agent Register a sub-agent
list_sub_agents List sub-agents (optionally recursive)
get_team_directory Get workspace team directory
update_agent_profile Update agent role, capabilities, profile

Governance & Config

Tool Description
get_project_rules Get all project rules
get_assignment_rules Get assignment rules
get_workflow_rules Get workflow rules with caller permissions
import_workspace_config Import workspace config from YAML
export_workspace_config Export workspace config as YAML

Recurring Tasks

Tool Description
create_recurring_task Create recurring task schedule
list_recurring_schedules List recurring schedules
get_recurring_history Get instance history for a schedule
trigger_recurring_now Trigger next instance immediately

Architecture

AI Agent (Claude Code / Cursor / Cline / OpenClaw)
    ↕ MCP (stdio or SSE)
EVC Mesh MCP Server (core or full profile)
    ↕ REST API (HTTP)
EVC Mesh API Server
    ↕
PostgreSQL / Redis / NATS / S3

The MCP server is a lightweight proxy - it translates MCP tool calls into REST API requests. No direct database access needed.

Deploy checklist (prod server - no CI)

evc-mesh-mcp has no CD pipeline; deploys are manual. Mandatory order (per
CLAUDE-workflow.md §1b Deploy Discipline):
migrate (goose up)binary swaprestart. Never swap the binary before migrations pass.

# 1. Build for the prod target
GOOS=linux GOARCH=amd64 go build -o evc-mesh-mcp .

# 2. Copy binary to prod
scp evc-mesh-mcp root@prod-host:/opt/evc-mesh-mcp/evc-mesh-mcp.new

# 3. On the prod host: run migrations FIRST, then swap binary
ssh root@prod-host

  # STEP 1 — Run evc-mesh DB migrations (mcp server reads the same DB).
  # goose CLI is not installed on the host — use the official docker image.
  # If this exits non-zero, STOP — do NOT swap the binary.
  DB_URL=$(grep ^DATABASE_URL /opt/evc-mesh/.env.prod | cut -d= -f2-)
  docker run --rm --network host \
    -v /opt/evc-mesh/migrations:/migrations \
    ghcr.io/pressly/goose:latest \
    goose -dir /migrations postgres "$DB_URL" up

  # STEP 2 — Swap binary (only after migrations succeed)
  mv /opt/evc-mesh-mcp/evc-mesh-mcp /opt/evc-mesh-mcp/evc-mesh-mcp.bak.$(date +%Y%m%d-%H%M%S)
  mv /opt/evc-mesh-mcp/evc-mesh-mcp.new /opt/evc-mesh-mcp/evc-mesh-mcp

  # STEP 3 — Restart
  sudo systemctl restart evc-mesh-mcp

  # STEP 4 — Smoke test
  curl -sf http://localhost:8081/health || echo "SMOKE FAILED"

evc-mesh-mcp does not run its own migrations - it relies on the evc-mesh API's
schema. The goose step above ensures the schema matches before the new binary serves traffic.

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Official By maintainer
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