Jacksun911

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: cancel_order, create_order, get_order, list_orders, pricing, and status each target specific actions or queries in the token launch workflow. The descriptions reinforce distinct functions, making misselection unlikely.
Naming Consistency5/5
All tools follow a consistent 'megalaunch_verb_noun' pattern with snake_case throughout, using clear verbs like cancel, create, get, list, and nouns like order, pricing, status. This predictability aids agent understanding and usage.
Tool Count5/5
With 6 tools, the set is well-scoped for managing token launches, covering core operations (create, cancel, query orders), pricing info, and service status. Each tool earns its place without bloat or thin coverage.
Completeness4/5
The tool surface provides strong coverage for the token launch domain, including CRUD-like operations (create, get, list, cancel), pricing, and status checks. A minor gap exists in lacking an update_order tool for modifying pending orders, but agents can work around this by canceling and recreating.
Average 3.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return values ('Returns deposit address and amount to send'), which is helpful, but lacks critical details like whether this is a read-only or destructive operation, permission requirements, rate limits, or error handling. For a creation tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first clause and efficiently adds return information in the second clause. It uses minimal words to convey essential information without redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a creation tool with 6 parameters and no output schema, the description is moderately complete. It covers the action and return values but lacks behavioral context (e.g., side effects, authentication) and doesn't fully compensate for the absence of annotations, leaving gaps in understanding the tool's full implications.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining interactions between parameters (e.g., how 'aiPick' relates to 'tokenName' and 'tokenSymbol'). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new meme token launch order') and the platform ('on Solana/pump.fun'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like megalaunch_cancel_order or megalaunch_get_order, which would require mentioning it's for initial creation versus modification or retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like megalaunch_cancel_order or megalaunch_get_order. It states what it does but offers no context for selection among siblings, such as prerequisites or scenarios where this is the appropriate choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'Get[s] detailed status', which implies a read-only operation, but doesn't clarify permissions, rate limits, error handling, or what 'detailed status' entails (e.g., format, included fields). This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple lookup tool, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on behavioral traits and usage context. Without annotations or output schema, more guidance on what 'detailed status' includes would improve completeness, but it's not severely lacking for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'orderId' fully documented in the schema. The description adds no additional parameter semantics beyond implying it's for a 'specific token launch order', which aligns with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'detailed status of a specific token launch order', making the purpose explicit. However, it doesn't differentiate from sibling tools like 'megalaunch_status' or 'megalaunch_list_orders', which likely provide related status information, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'megalaunch_status' or 'megalaunch_list_orders'. It mentions 'specific token launch order', which implies usage for individual orders, but lacks explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions listing orders with filtering but fails to disclose critical traits like whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior beyond the schema, or what the output format looks like. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('List your token launch orders') and adds a key feature ('with optional status filter'). There is no wasted text, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a listing tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return values, error conditions, or behavioral aspects like pagination handling, leaving significant gaps for an AI agent to understand how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (status, limit, offset) with descriptions and constraints. The description adds minimal value by hinting at the 'status' filter but doesn't provide additional semantics beyond what the schema offers, such as example status values or usage tips.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('your token launch orders'), making the purpose specific and understandable. It distinguishes itself from siblings like 'megalaunch_get_order' (singular) and 'megalaunch_create_order' (creation), but doesn't explicitly mention how it differs from 'megalaunch_status' or 'megalaunch_pricing'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'optional status filter', suggesting it's for retrieving orders with possible filtering. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'megalaunch_get_order' for single orders or 'megalaunch_status' for status checks, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves pricing, implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns real-time or cached data, or handles errors. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose ('Get current pricing') and includes essential details (resource and package types). There is no wasted language, and it is appropriately sized for a tool with no parameters, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally complete. It covers the purpose and resource but lacks behavioral details (e.g., authentication needs, data freshness) and usage guidelines. For a read-only tool, this is adequate but leaves gaps that could hinder an agent's effective use without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description adds value by specifying the resource (token launch packages) and package types (Basic and Premium), which provides context beyond the empty schema. Baseline for 0 parameters is 4, as it adequately compensates for the lack of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get current pricing') and resource ('token launch packages'), specifying the package types (Basic and Premium). It distinguishes this tool from siblings like megalaunch_create_order or megalaunch_get_order by focusing on pricing rather than order management. However, it doesn't explicitly differentiate from megalaunch_status, which might also provide pricing-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing authentication or account status, or when to choose it over siblings like megalaunch_status for pricing inquiries. Usage is implied (e.g., to check prices before creating an order), but no explicit context or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'checks' status and statistics, implying a read-only operation, but doesn't clarify authentication needs, rate limits, data freshness, or what 'recent' means. This leaves significant behavioral gaps for a monitoring tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of it contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description is minimally adequate. However, for a status-checking tool, it lacks details on what 'recent launch statistics' includes or the response format, leaving gaps in contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds no parameter information, which is appropriate here, earning a baseline score of 4 for not needing to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('check') and resources ('MegaLaunch service status and recent launch statistics'). It distinguishes this from sibling tools that focus on order management and pricing, though it doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like checking order status via sibling tools. It implies usage for status monitoring but lacks explicit when/when-not instructions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool cancels orders and has a constraint (unpaid orders only), which is useful. However, it doesn't describe potential side effects (e.g., whether cancellation is reversible), error conditions, or response format, leaving behavioral gaps 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the key action and constraint, making it easy to parse. Every word earns its place by contributing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is adequate but incomplete. It covers the purpose and usage constraint well, but lacks details on behavioral outcomes (e.g., what happens post-cancellation) or error handling, which are important given the tool's destructive nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'orderId' fully documented in the schema. The description doesn't add any parameter details beyond what the schema provides, such as format examples or validation rules. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Cancel') and target resource ('a pending token launch order'), and distinguishes it from siblings by specifying the scope ('only works for unpaid orders'). This provides precise differentiation from tools like megalaunch_get_order or megalaunch_list_orders.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('only works for unpaid orders'), providing clear context and exclusion criteria. This helps the agent avoid using it for paid orders, which is crucial guidance given the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![megalaunch-mcp MCP server](https://glama.ai/mcp/servers/jacksun911/megalaunch-mcp/badges/card.svg)](https://glama.ai/mcp/servers/jacksun911/megalaunch-mcp)

Score Badge

Copy to your README.md:

[![megalaunch-mcp MCP server](https://glama.ai/mcp/servers/jacksun911/megalaunch-mcp/badges/score.svg)](https://glama.ai/mcp/servers/jacksun911/megalaunch-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/jacksun911/megalaunch-mcp'

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