base-gasless-deploy-mcp

Server Quality Checklist

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

Latest release: v1.0.2
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: deploy_gasless_token creates tokens, estimate_gas_savings calculates gas savings, get_token_info retrieves token details, list_deployed_tokens lists deployments, and transfer_tokens handles transfers. The descriptions reinforce these distinct functions, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case: deploy_gasless_token, estimate_gas_savings, get_token_info, list_deployed_tokens, and transfer_tokens. This predictability aids agent understanding and tool selection without confusion.
Tool Count5/5
With 5 tools, the count is well-scoped for the server's purpose of gasless token deployment and management on Base. Each tool earns its place by covering essential operations without bloat, aligning with typical MCP server sizes of 3-15 tools.
Completeness4/5
The tool set provides strong coverage for the domain, including deployment, estimation, info retrieval, listing, and transfers, with clear CRUD-like operations. A minor gap exists in lacking explicit update or delete tools for tokens, but agents can likely work around this given the deployment focus.
Average 3.6/5 across 5 of 5 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.
Add a glama.json file to provide metadata about your server.
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 states the tool lists tokens and tracks deployments, but doesn't disclose critical behaviors such as whether this is a read-only operation, potential rate limits, session-specific constraints, or what the output format looks like (e.g., list structure, pagination). This leaves significant gaps for an agent to understand how to handle results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that efficiently convey the core purpose and scope. It's front-loaded with the main action and avoids unnecessary details. However, it could be slightly more structured by explicitly separating purpose from behavioral context.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., token identifiers, deployment timestamps), session behavior implications, or error conditions. For a tool with one parameter but no structured output guidance, this leaves the agent under-informed about how to interpret results.
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 'deployer_address' well-documented in the schema as 'Deployer wallet address to filter by'. The description adds no additional parameter semantics beyond what the schema provides, such as address format examples or filtering nuances. This meets the baseline for high schema coverage but doesn't enhance understanding.
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 ('List tokens deployed') and resource ('by an address'), specifying the scope ('in the current session'). It distinguishes from siblings like 'get_token_info' by focusing on deployment tracking rather than general token information. However, it doesn't explicitly differentiate from all siblings (e.g., 'transfer_tokens' is clearly different, but the distinction could be more explicit).
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 when tracking deployments made through the MCP server, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_token_info' for general token details. It mentions the context ('current session') but lacks clear when-not-to-use scenarios or prerequisites beyond the required address parameter.
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 the tool shows gas savings 'saved (or would be saved)', hinting at both retrospective and predictive analysis, but lacks details on permissions, rate limits, response format, or error handling. For a tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, though it could be slightly more structured by separating usage context from core functionality.
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 moderate complexity (involving gas savings analysis), no annotations, and no output schema, the description is minimally adequate. It covers the core purpose but lacks details on behavioral traits, output format, and integration with sibling tools, leaving gaps for an AI agent to fully understand usage.
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?
The schema description coverage is 100%, with the single parameter 'token_address' fully documented in the schema as 'Token contract address to check gas savings for'. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3.
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: 'Show how much gas was saved (or would be saved) by using the paymaster for a token deployment.' It specifies the action (show gas savings), resource (token deployment), and mechanism (using paymaster). However, it doesn't explicitly differentiate from sibling tools like 'deploy_gasless_token' or 'get_token_info', which prevents a score of 5.
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 'token deployment' and 'paymaster', suggesting this tool is for analyzing gas savings related to deployments. However, it provides no explicit guidance on when to use this versus alternatives like 'deploy_gasless_token' (for actual deployment) or 'get_token_info' (for general token data), nor does it specify prerequisites or exclusions.
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 full burden for behavioral disclosure. It states the tool retrieves information (implied read-only), but doesn't mention critical aspects like authentication requirements, rate limits, network dependencies (Base), error conditions, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational 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 and lists specific data points without unnecessary words. Every element ('Get info', token type, data fields) contributes directly to understanding the tool's function, making it appropriately sized and zero-waste.
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 no annotations and no output schema, the description is incomplete for a tool interacting with blockchain data. It lacks details on authentication, rate limits, error handling, and the structure of returned information (e.g., format of balances). While it specifies the data fields, operational context and output expectations are insufficiently covered.
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 'token_address' documented in the schema as 'Token contract address on Base'. The description doesn't add meaning beyond this, as it doesn't elaborate on address format, validation, or examples. With high schema coverage, the baseline score of 3 is appropriate, though no extra value is provided.
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 ('Get info about') and resource ('a deployed ERC-20 token'), listing the exact data returned (name, symbol, decimals, total supply, owner balance). It distinguishes from siblings like 'deploy_gasless_token' (creation) and 'transfer_tokens' (transaction) by focusing on read-only information retrieval.
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 when token metadata is needed, but provides no explicit guidance on when to use this tool versus alternatives like 'list_deployed_tokens' (which likely returns multiple tokens without detailed info). No exclusions or prerequisites are mentioned, leaving usage context partially inferred rather than clearly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key traits: the gasless mechanism with paymaster, fallback behavior, and a critical requirement ('Requires DEPLOYER_PRIVATE_KEY'). It lacks details on error handling, rate limits, or response format, but covers essential operational aspects.
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 phrase, followed by key behavioral details and requirements in just two sentences. Every sentence adds critical information without waste, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a deployment tool with no annotations or output schema, the description provides good coverage of purpose, behavior, and requirements. It could improve by detailing the return value or error cases, but it adequately informs the agent for basic usage in context with the schema.
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. The description does not add specific meaning beyond the schema, such as explaining interactions between parameters or usage nuances. The baseline score of 3 reflects adequate but no extra value from the description.
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 ('Deploy an ERC-20 token'), target platform ('on Base'), and mechanism ('using CDP Paymaster (zero gas for user)'). It distinguishes from sibling tools like 'list_deployed_tokens' or 'get_token_info' by focusing on deployment rather than querying or transferring.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Deploy an ERC-20 token on Base using CDP Paymaster') and mentions a fallback scenario ('Falls back to normal gas if no paymaster configured'). However, it does not explicitly contrast with alternatives or specify when not to use it, such as compared to other deployment methods or sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key traits: the gasless mechanism via paymaster, fallback to normal gas, and the requirement for DEPLOYER_PRIVATE_KEY (indicating auth needs and that transfers are from a specific wallet). It lacks details on rate limits, error handling, or what happens if paymaster fails, but covers essential operational context adequately.
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, followed by key behavioral details in two concise sentences. Every sentence adds value: the first defines the action and mechanism, the second covers fallback and prerequisites, with no wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (token transfer with gasless logic), no annotations, and no output schema, the description is mostly complete. It covers the main action, mechanism, fallback, and auth needs, but lacks details on return values, error cases, or specific behavioral outcomes (e.g., transaction hash or confirmation), which could be beneficial for an agent.
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 three parameters (token_address, to, amount) with clear descriptions. The description does not add any parameter-specific details beyond what the schema provides, such as format examples or constraints, meeting the baseline for high schema coverage.
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 ('transfer ERC-20 tokens gaslessly via paymaster') and resource (tokens), distinguishing it from siblings like deploy_gasless_token (creation), estimate_gas_savings (estimation), get_token_info (query), and list_deployed_tokens (listing). It specifies the fallback behavior and source wallet, making the purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (for gasless token transfers with a fallback) and mentions a prerequisite (DEPLOYER_PRIVATE_KEY). However, it does not explicitly state when not to use it or name alternatives among siblings, such as using estimate_gas_savings for pre-transfer checks or get_token_info for token details, leaving some guidance implicit.
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:

[![base-gasless-deploy-mcp MCP server](https://glama.ai/mcp/servers/lordbasilaiassistant-sudo/base-gasless-deploy-mcp/badges/card.svg)](https://glama.ai/mcp/servers/lordbasilaiassistant-sudo/base-gasless-deploy-mcp)

Score Badge

Copy to your README.md:

[![base-gasless-deploy-mcp MCP server](https://glama.ai/mcp/servers/lordbasilaiassistant-sudo/base-gasless-deploy-mcp/badges/score.svg)](https://glama.ai/mcp/servers/lordbasilaiassistant-sudo/base-gasless-deploy-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/lordbasilaiassistant-sudo/base-gasless-deploy-mcp'

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