radtask-mcp
Server Details
AI task cockpit: an agent does your tasks and asks before anything irreversible.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- dougsureel-tech/radtask-mcp
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.6/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: create, list, and complete tasks. No overlap or ambiguity.
All tools follow a consistent verb_noun pattern (create_task, list_tasks, complete_task) with snake_case.
3 tools is minimal but appropriate for a focused task management server; each tool serves a necessary function without being overly sparse.
Covers basic create, list, and status update (complete) but lacks update and delete operations, which are notable gaps for full task lifecycle management.
Available Tools
3 toolscomplete_taskAInspect
Mark a RadTask task done by its id (from list_tasks).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It only indicates a state change ('Mark done') but does not mention whether the change is reversible, idempotent, or has side effects. This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the essential action without extraneous words. Every part serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (1 param, no output schema), the description covers the basic purpose and parameter source. However, it lacks details about the result of the action (e.g., success/failure, state change confirmation) and potential side effects, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description adds meaning to the only parameter 'id' by specifying it is obtained 'from list_tasks'. This provides source context beyond the type string, helping the agent understand how to populate the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Mark a RadTask task done' and the resource 'task by its id'. It distinguishes from siblings create_task and list_tasks by focusing on completion. The reference to list_tasks provides context for obtaining the id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that the id comes from list_tasks, hinting at a prerequisite. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide when-not guidance. Some usage context is implied but not detailed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_taskAInspect
Add a task to the user's RadTask cockpit. Use this to capture anything the user wants done or tracked.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | The task in plain English | |
| project | No | Optional project to group it under |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, so description must disclose behavior. Only states 'Add a task' which implies write operation, but no mention of side effects, permissions, reliability, or return values. Inadequate for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action, no redundant words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool (2 params, no output schema). Description covers purpose and usage adequately. Lacks behavioral details (e.g., idempotency, confirmation), but given low complexity, it is mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear descriptions for 'title' and 'project'. Description adds general context but does not enhance parameter meaning beyond schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Add', resource 'task to RadTask cockpit', and scope 'anything user wants done or tracked'. Distinguishes from sibling tools (complete_task, list_tasks) as it focuses on creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear guidance on when to use ('capture anything the user wants done or tracked') but lacks explicit exclusions or comparison to siblings. Context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tasksBInspect
List the user's RadTask tasks, optionally filtered by status.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It states 'list' implying a read-only operation but does not explicitly confirm non-destructiveness, mention pagination, ordering, or any side effects. This is insufficient for a mutation-free tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no fluff. It front-loads the core action and scope. However, it could be slightly longer to include critical missing details without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional param, no output schema), the description covers basic purpose but lacks behavioral and parameter details. It is minimally adequate but not robustly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain the parameter 'status' and its enum values. It only says 'optionally filtered by status' without defining what each status (todo, running, etc.) means. This adds negligible value over the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (list), resource (RadTask tasks), and scope (user's tasks). It differentiates itself from sibling tools like complete_task and create_task, establishing its role as a read-only listing tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions optional status filtering but provides no explicit guidance on when to use this tool versus siblings. While the purpose is implicit, it lacks direct statements like 'Use this to view tasks; use create_task to add new ones.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- Alicense-qualityAmaintenanceLocal-first AI agent for approval-gated automation and verifiable LLM workflows.1MIT
- AlicenseAqualityBmaintenanceEnables AI agents to manage a task board by investigating needs, creating well-specified tasks, and executing them end-to-end.6MIT
- FlicenseAqualityBmaintenanceA local-first MCP server that handles daily work tasks through your AI assistant: converts meeting notes into todos, manages todo lifecycle, tracks work hours, generates daily/weekly reports, organizes files via move-only operations, and diagnoses dev environments, all guarded by a human-maintained preview/apply safety model.25
- Flicense-qualityDmaintenanceA unified AI and automation command center that allows users to interact with multiple AI providers (OpenAI, Anthropic, Gemini) and services (GitHub, Google) through natural language commands and multi-step workflows.