Skip to main content
Glama

Orcha MCP server

Orcha is a unified context layer for AI tools and agents. It stores, indexes, and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and agents reach them through Model Context Protocol (MCP), CLI with virtual file system, or REST API, with permissions, citations, and provenance.

This repository is the public connection guide and registry listing for Orcha's hosted MCP server. The service runs at:

https://app.tryorcha.com/mcp

Transport is Streamable HTTP. The application source code is private; nothing needs to be installed or built from this repository to use the server.

What the server provides

28 tools spanning:

  • Usage guides the agent can read for itself (get_usage_guide), covering tool choice, search tuning, citations, writing back, and troubleshooting

  • Ranked search over all indexed workspace content (search_context) with fast, balanced, and fresh routing modes, plus a citation on every result

  • File and folder reading, creation, update, organization, and deletion, scoped to what the connection is allowed to touch

  • Curated memories with usage guidance (list_memories, get_memory), including agent-side curation (create_memory, update_memory) covering files, folders, synced documents, and database views

  • Structured databases with typed queries (list_databases, query_database, record CRUD)

  • Direct access to connected read-only sources (query_source, fetch_source_document, list_source_documents)

  • Filesystem-shaped browsing (browse_context): ls, tree, find, cat, grep, and stat over a virtual tree of the workspace

Every connection is isolated to the organization, workspace, permissions, and optional folder scope it was granted.

The server also serves its usage guides as Agent Skills under the draft io.modelcontextprotocol/skills extension: skills/list enumerates them and each file is readable at skill://orcha/<name>/<path>. Clients without skills support get the same content from the get_usage_guide tool.

Related MCP server: ContextAtlas

Add the server URL to any OAuth-capable MCP client. The client discovers Orcha's authorization server automatically and walks you through consent; the grant is scoped to the organization and workspace you approve.

Claude Code:

claude mcp add --transport http orcha "https://app.tryorcha.com/mcp"

claude.ai and Claude Desktop: Settings, Connectors, Add custom connector, then enter https://app.tryorcha.com/mcp.

Codex takes TOML rather than JSON. Add this to ~/.codex/config.toml, or .codex/config.toml in a trusted project:

[mcp_servers.orcha]
url = "https://app.tryorcha.com/mcp"
auth = "oauth"

Codex also reads Agent Skills from .agents/skills/ in a repository or ~/.agents/skills/ globally, so Orcha's four usage guides can be installed there. They are available with no installation through get_usage_guide.

Cursor and other OAuth-capable clients: add a remote MCP server with the same URL and follow the sign-in prompt.

Claude Code users can instead install the plugin below, which does the same connection and adds the usage guides as skills.

Connect with a bearer token (headless and CI clients)

  1. Create an Orcha account and workspace at app.tryorcha.com/signup.

  2. Create an API token scoped to that workspace with only the permissions the client needs. Tokens begin with orca_.

  3. Configure the client with an Authorization header:

{
  "mcpServers": {
    "orcha": {
      "url": "https://app.tryorcha.com/mcp",
      "type": "http",
      "headers": {
        "Authorization": "Bearer orca_your_token"
      }
    }
  }
}

Example prompts

  • "Search my workspace for the current authentication requirements and summarize them with the source file names."

  • "Show me the five files most related to our deployment runbook."

  • "Check which Orcha memories apply to this task and use the best match as the source of truth."

  • "Create a Markdown file named launch-checklist.md from these release requirements."

  • "Get the engineering-onboarding memory and identify any conflicting guidance."

Claude Code plugin

Claude Code can install the connection and the usage guides together:

/plugin marketplace add westonhancock/orcha-mcp
/plugin install orcha@orcha

The plugin carries its own server configuration, so it replaces the claude mcp add step above rather than following it. It also installs the four usage guides as skills, so Claude loads the relevant one when a task calls for it. See plugins/orcha for details.

This is a convenience for one client. Every other client gets the same guidance from the server through get_usage_guide.

Registry listing

The server is published in the Official MCP Registry as io.github.westonhancock/orcha. The manifest in server.json mirrors the registry entry. A machine-readable server card is served at https://app.tryorcha.com/.well-known/mcp/server-card.json.

F
license - not found
Not graded
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI coding agents to retrieve and manage code context with hybrid search, project memory, and observability via MCP tools.
    29
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    A governed context layer for internal data exposed over MCP, enabling agents to query governed definitions, retrieve grounded data, and execute scoped actions with citations and auditing.
    Apache 2.0

View all related MCP servers

Related MCP Connectors

  • Shared, permission-aware company context for AI agents, with provenance, approvals and audit.

  • The personal context layer for AI: your profile and files, read by any MCP client over OAuth.

  • Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/westonhancock/orcha-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server