my-minimax-mcp

by wongo

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Server Quality Checklist

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Latest release: v1.5.1
Disambiguation5/5
Each tool has a clearly distinct purpose: autonomous coding, chat, cost reporting, code generation, planning, session tracking, image analysis, and web search. No overlaps or ambiguity.
Naming Consistency5/5
All tools follow the consistent pattern 'minimax_<descriptive_name>', using snake_case and a clear verb-noun structure throughout.
Tool Count5/5
8 tools is well-scoped for a server providing multiple MiniMax AI capabilities. It covers a broad range of functionalities without being overwhelming or too sparse.
Completeness5/5
The tool set covers the major use cases of the MiniMax MCP server: autonomous task execution, chat, code generation, planning, cost tracking, session monitoring, image analysis, and web search. No obvious gaps given the stated purpose.
Average 3.6/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 28 commits in the last 12 weeks
- Last stable release on June 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
No annotations are present, so the description must fully disclose behavioral traits. It fails to mention whether the plan is purely text-based, any constraints (e.g., token limits, speed), or side effects like modifications to the codebase. The description is too terse for a tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. However, it could include more essential information (e.g., output format, usage note) without becoming verbose.
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?
No output schema exists, so the description should describe the structure of the generated plan. It does not, leaving agents uninformed about return values. For a planning tool, this is a significant gap.
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 coverage is 100%, so the input schema already describes all three parameters (task, codebaseContext, model). The description adds no additional meaning beyond 'Generate...plan as JSON', which aligns with the task parameter but does not augment parameter understanding. Baseline of 3 is appropriate.
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 generates a structured implementation plan as JSON, using a specific verb (Generate) and resource (implementation plan). It is distinguishable from siblings like minimax_generate_code, which focuses on code generation, but the description could be more explicit about what kind of plan is produced.
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?
No guidance is provided on when to use this tool versus alternatives such as minimax_generate_code or minimax_chat. The description lacks context about prerequisites, typical use cases, 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?
With no annotations provided, the description bears full burden. It mentions output fields but omits critical behavioral traits such as rate limits, authentication needs, handling of empty results, or whether the operation is read-only. The description is minimally transparent.
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 consists of two short, front-loaded sentences with no wasted words. Every sentence conveys distinct information: the tool's purpose and its output structure.
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 simple tool with one parameter, the description covers purpose and basic output. However, it fails to specify result count, pagination, possible errors, or behavioral constraints. Given no output schema or annotations, more detail would be beneficial.
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 coverage is 100% (single 'query' parameter described as 'Search query'). The description adds value by listing output fields but does not further elaborate on the query parameter itself. Baseline 3 is appropriate.
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 tool's action (search the web using MiniMax AI) and what it returns (titles, links, snippets, related searches). It sufficiently distinguishes from sibling tools like minimax_chat or minimax_understand_image.
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?
No guidance on when to use this tool versus alternatives. No explicit context on when not to use it, prerequisites, or comparisons with sibling tools like minimax_plan or minimax_generate_code.
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 full burden. It discloses multi-turn capability and context preservation, but lacks details on auth, rate limits, or any side effects.
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, concise sentence that efficiently conveys the tool's purpose and key feature without redundancy.
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 simple 4-parameter chat tool with no output schema, the description covers the core functionality but does not explain return format, error handling, or advanced usage scenarios.
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 adds minimal extra meaning beyond the schema descriptions.
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 tool is for multi-turn chat with MiniMax AI and emphasizes conversation context preservation, which distinguishes it from sibling tools like minimax_agent_task or minimax_generate_code.
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 for chat with context preservation but does not explicitly contrast with alternative tools or state when not to use it.
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?
With no annotations provided, the description carries the full burden. It discloses supported image formats and size limits, and mentions the model override capability. However, it does not state whether the operation is read-only, what the output type is (e.g., text), or behavioral nuances like error handling or idempotence.
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 two essential sentences: the first states the core purpose, and the second provides key constraints on inputs. No redundant text. It is front-loaded with the main action and uses efficient wording.
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?
Despite having no output schema, the description omits any mention of what the tool returns (e.g., a text description, analysis results, or error messages). It also does not address prerequisites, potential errors, or limitations beyond format and size. This gap is significant for an image analysis tool.
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?
Schema description coverage is 100%, so baseline is 3. The description adds value by specifying valid input formats for imageSource (URLs, local paths, base64, with format and size constraints), which goes beyond the schema's generic 'Image URL' description. For prompt and model, the description adds little beyond the schema, but the extra detail on imageSource justifies a 4.
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 tool's purpose: 'Analyze an image using MiniMax AI vision.' It distinguishes from sibling tools like minimax_chat and minimax_generate_code by specifically focusing on image analysis, and the mention of supported input formats further clarifies its scope.
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 minimax_agent_task or minimax_chat. It does not specify use cases, prerequisites, or exclusions, leaving the agent to infer suitability from the tool's name and basic function.
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 states the tool provides a report and mentions offloaded tokens, but does not disclose whether it is read-only, any required permissions, or rate limits. The description is adequate but not explicit.
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?
Two sentences, no filler, front-loaded with the core purpose. Every sentence provides essential information.
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?
No output schema, so the description should hint at return format. It mentions what is shown (cost, token usage, savings, offloaded tokens) but does not specify if the report is text or structured. Given the tool's simplicity, this is nearly complete.
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?
There are zero parameters, and schema description coverage is trivially 100%. The description adds no parameter information, but none is needed. Baseline 3 is appropriate.
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 identifies the tool's purpose: 'Get a cost, token usage, and savings report for this session.' It specifies the verb 'Get' and the resource 'cost, token usage, and savings report', and further distinguishes from siblings by highlighting MiniMax offloading context.
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?
No explicit guidance on when to use or avoid this tool. The sibling tools are all generative/action-oriented, so it's implicitly used for reporting, but no exclusions or alternatives are stated.
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 discloses the main behavioral traits: code generation and optional file writing. It doesn't mention authentication, rate limits, or error handling, but the core behavior is clear.
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 key action and optional behavior, with no wasted words.
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?
No output schema exists, and the description fails to explain the return format or structure of the generated code beyond 'returns generated code'. This is a gap for a code generation tool that might return complex objects.
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 coverage is 100%, and the description adds no additional meaning beyond what the input schema already provides for each parameter. The baseline of 3 applies.
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 verb 'Generate' and the resource 'code using MiniMax AI', and distinguishes from sibling tools like minimax_chat and minimax_agent_task which serve different purposes.
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 for code generation but provides no explicit guidance on when to use this tool versus alternatives like minimax_agent_task or minimax_chat, nor any prerequisites or exclusions.
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 full burden. It discloses that 'start' is auto-called on first use and 'end' records optional notes, which are behavioral traits. However, it does not mention data persistence, rate limits, or any side effects beyond logging.
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 two sentences, front-loading the main purpose and briefly listing commands with context. It is efficient and avoids redundancy, though a more structured format (e.g., bullet points) could enhance readability.
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?
The description explains commands but does not describe return values or output format, which is important for an agent to interpret results (e.g., what trend analytics look like). No output schema exists, so the description should compensate but does not.
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?
Schema description coverage is 100%, so the schema already describes parameters. The description adds value by explaining 'start' is auto-called and 'status' provides trend analytics, going beyond the schema's brief descriptions. The 'notes' parameter's purpose for root cause is reinforced.
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 tool tracks MiniMax usage across sessions, with three specific commands (start, end, status) and brief explanations of each. It distinguishes itself from sibling tools by focusing on session tracking for self-improvement, not general tasks or chat.
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 explains when to use each command (start auto-called, end for recording, status for progress), but does not explicitly provide guidance on when to use this tool vs alternatives or when not to use it. Sibling tools are distinct, so implied usage is clear but not explicit.
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?
Given no annotations, the description carries the full burden. It discloses the tool's autonomous loop behavior: reading files, writing code, running tests, debugging. This provides good transparency for an autonomous agent 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?
Two efficient sentences with no wasted words. The first sentence front-loads the purpose, and the second adds behavioral detail. Every sentence earns its place.
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?
For a tool with a clear autonomous loop and fully described parameters, the description covers the main behavior and scope. It could mention error handling or cancellation, but is sufficiently complete for an agent to understand its primary function.
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 tool description does not add significant meaning beyond the schema descriptions. All parameters are described in the schema, so with 100% coverage, the baseline is 3. The description provides context but no parameter-specific enhancements.
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 'execute a complete coding task autonomously', using a specific verb and resource. It distinguishes from siblings like minimax_generate_code by emphasizing the autonomous loop that includes reading, writing, testing, and debugging.
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 the tool is for full coding tasks, but does not explicitly state when to use it versus alternatives like minimax_chat or minimax_plan. No when-not or alternative comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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