A
licenseA
qualityD
maintenanceProvides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
7
22 npm
1
MIT
agent-ops-mcp
by yulechkamsk1
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agent-ops-mcp Run the quality gate on the task output before I review it."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
agent-ops-mcp
A demo MCP server showing the shape of a setup I built for my team: an agent that reads and writes to our internal systems directly, instead of someone pasting context into a chat by hand, plus a lightweight quality gate that checks what the agent produced before it reaches a human reviewer.
The real version talks to our actual task tracker, internal docs, and CI — that code isn't public. This repo is a self-contained rebuild of the same idea against mock data, so it runs standalone with no credentials or access to anything internal.
What it exposes
Tool | Does |
| List tasks from a mock tracker, filterable by status/assignee |
| Fetch a single task by id |
| Create a task |
| Full-text search over a small mock internal wiki |
| Fetch a doc's full content by id |
| Heuristic pre-review gate — flags hardcoded secrets, leftover |
review_check is the interesting one: instead of a human catching a stray
TODO or a hardcoded password during review, the agent runs this on its own
output first. It mirrors the review-checklist doc bundled in this repo.
Related MCP server: MCP Document & Task Server
Run it
npm install
npm run build
npm startThis starts an MCP server over stdio. To try it from Claude Desktop or Claude Code, add it to your MCP config:
{
"mcpServers": {
"agent-ops-mcp": {
"command": "node",
"args": ["/absolute/path/to/agent-ops-mcp/dist/index.js"]
}
}
}For local development without a build step:
npm run devData
Everything lives in memory, seeded from src/data/tasks.ts and
src/data/docs.ts on startup. create_task mutates the in-memory list —
nothing is written to disk, so state resets every run.
Stack
TypeScript, Node.js, @modelcontextprotocol/sdk, Zod for input validation.
License
MIT
Available Tools
6 toolscreate_taskCreate taskC
Create a new task in the tracker.
Inputs: title (required), assignee (required), tags, priority.
get_docGet docA
Fetch the full content of a doc by id (from search_docs results).
Inputs: id (required).
get_taskGet taskB
Get a single task by id.
Inputs: id (required).
list_tasksList tasksA
List tasks from the team tracker, optionally filtered by status or assignee.
Inputs: status, assignee.
review_checkPre-review quality gateA
Run heuristic checks (secrets, leftover TODOs, placeholder text, size) over generated text or a diff before it goes to a human reviewer — mirrors the team's pre-review checklist doc.
Inputs: text (required).
search_docsSearch internal docsA
Full-text search over the internal docs/wiki (style guide, runbooks, checklists). Stands in for hitting a real Confluence-style docs API.
Inputs: query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
A3.5/5.0
Scored across 6 tools
Disambiguation5/5
Each tool targets a distinct resource and action: tasks (list/get/create), docs (search/get), and a review check. There is no overlap or ambiguity between them. An agent can easily select the right tool for each intent.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (list_tasks, get_doc, review_check). The verbs are clear and the style is uniform throughout. This makes the API predictable and easy to extend.
Tool Count5/5
With only 6 tools, the surface is well-scoped and focused. Each tool earns its place by covering a necessary operation without bloat. This is an ideal size for an agent helper that combines tasks, docs, and quality checks.
Completeness3/5
Task management is incomplete: you can create and list tasks but cannot update, delete, or change status, which are common operations. Docs are read-only (search/get), which is acceptable for a reference. Adding update/delete for tasks would round out the lifecycle.
Maintenance
ActivityInactive
ResponsivenessNo recent community issues
Related MCP Connectors
Shared task layer for AI agents: tasks, handoffs and decisions that persist across tools.
Task manager for AI agents. Orchestrate agents to achieve large goals in unified control plane.
AI-native task management: list, create, update and archive tasks with rich context for AI agents
Project management for AI agents: tasks, docs, decisions and time in one shared team context.
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- AlicenseBqualityDmaintenanceProvides a document database with search, retrieval, and creation tools, and a task tracker with search and status filtering, enabling AI agents to manage documents and tasks via natural language.3MIT
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- FlicenseNot gradedqualityDmaintenanceExposes task management (add, list, complete tasks) and document search (RAG) as MCP tools for AI agents.-