aptos-mcp

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: generating ABI, creating indexers, creating projects, creating gas stations, generating components, and testing contracts. The descriptions specify different resources and actions, making misselection unlikely.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with 'aptos' as a prefix (e.g., create_aptos_project, generate_aptos_component), except for 'aptos_abi_generate' which uses noun_verb order. This minor deviation slightly breaks the pattern but maintains overall readability.
Tool Count5/5
With 6 tools, the count is well-scoped for an Aptos development server, covering key operations like project setup, component generation, and testing. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness3/5
The toolset covers creation and testing aspects well but has notable gaps in lifecycle management, such as updating or deleting projects/components, and lacks deployment or interaction tools (e.g., deploy_contract, call_function). This limits full workflow coverage for Aptos development.
Average 2.9/5 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
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Generate ABI') but fails to describe key traits: it doesn't mention if this is a read-only operation, what permissions are required, potential side effects (e.g., file creation), or error handling. This leaves significant gaps in understanding the tool's behavior.
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 front-loaded with the main purpose, followed by a brief 'Args' section listing parameters. It's efficient with minimal waste, though the structure could be slightly improved by integrating parameter details more seamlessly. Overall, it's appropriately sized for its content.
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 (a code generation tool with 2 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the ABI output entails, how it's delivered (e.g., file path or direct return), or error cases. This inadequacy could hinder effective tool use by 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?
The description adds basic semantics for both parameters: 'contract_path' is explained as 'Path to the contract directory' and 'output_format' as 'Format of the output (ts, json)'. Since schema description coverage is 0%, this compensates somewhat by clarifying parameter meanings. However, it lacks details like format specifics or path requirements, keeping it at a baseline level.
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: 'Generate ABI for an Aptos contract.' It specifies the verb ('Generate') and resource ('ABI for an Aptos contract'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generate_aptos_component' or 'test_aptos_contract', which may also involve contract-related operations, 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. It lacks context about prerequisites (e.g., needing a compiled contract), exclusions, or comparisons to sibling tools such as 'generate_aptos_component'. This absence leaves the agent without clear usage direction.
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. While 'Creates' implies a write operation, it doesn't specify permissions required, whether the creation is reversible, what happens on failure, or any rate limits. The mention of 'based on the example processor' adds some context but falls short of describing key behavioral traits like side effects or error handling.
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 appropriately sized with two sentences: one stating the purpose and another listing parameters. It's front-loaded with the main action, and each sentence adds value. There's no redundant information, though it could be slightly more structured (e.g., bullet points for parameters).
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, 0% schema description coverage, and no output schema, the description is incomplete. It covers basic purpose and parameters but lacks details on behavioral traits, error cases, return values, and differentiation from siblings. For a creation tool with two parameters, this leaves significant gaps for an agent 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 0%, so the description must compensate. It lists both parameters ('project_name' and 'processor_type') and provides brief meanings, which adds value beyond the bare schema. However, it doesn't explain constraints (e.g., format for 'project_name', valid values for 'processor_type' beyond 'transaction' and 'event'), leaving gaps in parameter 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 ('Creates a new Aptos indexer project') and specifies the resource ('based on the example processor'), which provides a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'create_aptos_project' or 'aptos_abi_generate', leaving some ambiguity about when to use this specific tool versus alternatives.
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. With sibling tools like 'create_aptos_project' and 'aptos_abi_generate' available, there's no indication of context, prerequisites, or exclusions. The agent must infer usage from the tool name alone, which is insufficient for clear decision-making.
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 mentions 'Create' which implies a write operation, but lacks details on permissions, side effects, error handling, or output format. This is insufficient for a mutation 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by a clear parameter breakdown in a structured format. Every sentence earns its place with no wasted words, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It fails to address critical aspects like behavioral traits, usage context, or return values, leaving significant gaps for an AI 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?
The description adds value by explaining the semantics of both parameters (project_name and project_type with its allowed values), which compensates for the 0% schema description coverage. However, it doesn't provide deeper context like format constraints or examples, keeping it at a baseline level.
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 'Create' and the resource 'new Aptos project using the Aptos CLI', which is specific and actionable. However, it doesn't explicitly distinguish this tool from its siblings (e.g., create_aptos_indexer, create_gas_station), which prevents a perfect 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, nor does it mention prerequisites or context for usage. It simply states what the tool does without indicating appropriate scenarios 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 the full burden of behavioral disclosure. It states the tool generates a component but doesn't describe what that entails—whether it creates files, modifies existing ones, requires specific permissions, or has side effects like initializing dependencies. For a tool with potential file system impacts, this lack of detail 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a clear purpose statement followed by a bullet-point list of parameters. Each sentence earns its place by defining the tool and its inputs. It could be slightly more front-loaded with key behavioral details, but overall it's efficient without unnecessary verbiage.
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 (generating components in a development environment), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't cover behavioral traits, output format, error handling, or dependencies on sibling tools. For a tool that likely interacts with file systems and project structures, more context is needed for safe and effective use.
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 description lists all four parameters with brief explanations, but schema description coverage is 0%, meaning the schema provides no additional details. The description adds basic semantics (e.g., 'Type of component (table, module, etc.)'), which compensates somewhat. However, it doesn't elaborate on allowed values for 'component_type' or format for 'options', leaving ambiguity. Baseline is adjusted upward from 1 due to parameter listing.
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: 'Generate a new component for an Aptos project.' It specifies the verb ('generate') and resource ('component for an Aptos project'), which is clear. However, it doesn't explicitly differentiate from sibling tools like 'create_aptos_project' or 'aptos_abi_generate', leaving some ambiguity about scope boundaries.
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 (e.g., whether an Aptos project must exist), compare to siblings like 'create_aptos_project' (which might handle project setup), or specify scenarios where this tool is appropriate. Usage is implied but not explicitly defined.
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 full burden for behavioral disclosure. It mentions using 'Aptos CLI' which provides some context about execution method, but doesn't describe what 'test' means operationally (e.g., runs unit tests, validates bytecode, requires compilation), expected outputs, error conditions, or resource requirements.
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 appropriately sized with a clear purpose statement followed by parameter explanations. The structure is front-loaded with the main purpose first. It could be slightly more concise by integrating the Args section more naturally, but overall it's efficient.
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?
For a 3-parameter tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'testing' entails, what results to expect, error handling, or how this integrates with the Aptos development workflow. The minimal parameter explanations don't compensate for the lack of behavioral context.
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 description provides brief explanations for all three parameters in the Args section, adding meaning beyond the schema which has 0% description coverage. However, these explanations are minimal ('Path to the contract directory or file', 'Optional function to test specifically') and lack details about format, constraints, or examples.
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 ('Test an Aptos Move contract') and resource ('Aptos Move contract'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'aptos_abi_generate' or 'create_aptos_project', which prevents a perfect 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. There's no mention of prerequisites, when testing is appropriate, or how this differs from sibling tools like 'create_aptos_project' or 'generate_aptos_component'.
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 creates a new project, implying a mutation operation, but lacks details on permissions, side effects, error handling, or response format. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and implications.
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 and well-structured, with a clear purpose statement followed by a brief parameter explanation in two sentences. It avoids unnecessary details and is front-loaded with the main action. Minor improvements could include integrating the parameter info more seamlessly, but overall it is efficient and readable.
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 complexity (a creation operation with one parameter), lack of annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and parameter semantics but misses behavioral details, usage guidelines, and output information. This leaves users with incomplete context for effective tool invocation.
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 description adds meaningful context for the single parameter 'project_name' by explaining it as the 'Name of the gas station project'. With schema description coverage at 0% (the schema only provides a title 'Project Name'), this compensates well by clarifying the parameter's role. However, it does not detail constraints like length or format, keeping it from a perfect score.
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: 'Creates a new Aptos gas station (fee sponsorship) project.' It specifies the verb ('creates') and resource ('gas station project'), with additional clarifying context ('fee sponsorship'). However, it does not explicitly differentiate from sibling tools like 'create_aptos_project' or 'create_aptos_indexer', which also involve creation operations in the Aptos ecosystem, leaving some ambiguity about when to use this specific tool.
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 does not mention prerequisites, exclusions, or compare it to sibling tools such as 'create_aptos_project' or 'create_aptos_indexer'. Without this context, users must infer usage based on the tool name and description alone, which is insufficient for clear decision-making.
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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