DigitalOcean MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes: call_digitalocean_api for API calls, configure_digitalocean_api for setup, and list_endpoints/search_endpoints for discovery. However, get_endpoint_details and list_endpoints could be slightly confusing as both relate to endpoint information, though descriptions clarify one is for details and the other for listing.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case (e.g., call_digitalocean_api, list_endpoints, search_endpoints). The naming is predictable and uniform across the set, making it easy to understand each tool's function at a glance.
Tool Count4/5
With 6 tools, the count is reasonable for a server focused on DigitalOcean API interaction. It covers configuration, discovery, and execution, though it might feel slightly thin if more advanced operations are expected, but it's well-scoped for its apparent purpose.
Completeness3/5
The tool set covers configuration, endpoint discovery, and API calls, but there are notable gaps. For a DigitalOcean server, it lacks direct tools for common resources like droplets, databases, or volumes, relying instead on generic API calls, which could lead to agent inefficiencies or errors in handling specific workflows.
Average 2.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 provided, so the description carries full burden for behavioral disclosure. It states the action ('call') but lacks critical details: authentication requirements, rate limits, error handling, or what 'call' entails (e.g., HTTP method, response format). For a generic API-calling tool with no structured safety hints, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It is appropriately sized for a simple tool, though it could be more informative. However, it lacks front-loading of critical details, which slightly reduces its effectiveness.
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 calling arbitrary API endpoints, no annotations, no output schema, and sibling tools suggesting varied operations, the description is incomplete. It fails to address authentication, error handling, or response structure, leaving the agent with insufficient context 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?
Schema description coverage is 100%, so the schema already documents both parameters ('operationId' and 'parameters'). The description adds no meaning beyond what the schema provides—it doesn't explain how to use 'operationId' or what 'parameters' should contain. 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.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Call a DigitalOcean API endpoint' restates the tool name with minimal additional specificity. It mentions the verb 'call' and resource 'DigitalOcean API endpoint', but fails to distinguish from siblings like 'configure_digitalocean_api' or 'get_endpoint_details', nor does it clarify what 'call' entails beyond the obvious. This is borderline tautological.
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. Sibling tools like 'configure_digitalocean_api', 'get_endpoint_details', and 'list_endpoints' suggest different purposes, but the description offers no context, prerequisites, or exclusions. Usage is implied only by the tool name, not explained.
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 but only states it 'gets' information, implying a read-only operation. It lacks behavioral details such as required permissions, error handling, rate limits, or what 'detailed information' includes (e.g., metadata, status). This is a significant gap 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 with zero waste—it directly states the tool's function without redundancy. It's appropriately sized and front-loaded, making it easy to parse quickly.
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. It doesn't explain what 'detailed information' entails (e.g., response structure), behavioral traits, or usage context. For a tool with 1 parameter and 100% schema coverage, it should do more to compensate for the lack of structured data.
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 the 'operationId' parameter. The description adds no additional meaning beyond implying it's for a 'specific endpoint', which aligns with the schema but doesn't provide extra context like format examples or usage tips. Baseline 3 is appropriate when 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 resource ('detailed information about a specific endpoint'), making the purpose understandable. It distinguishes from siblings like 'list_endpoints' by focusing on a single endpoint, but doesn't explicitly differentiate from 'search_endpoints' which might also retrieve endpoint details.
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 'list_endpoints' or 'search_endpoints'. It mentions 'specific endpoint' but doesn't clarify prerequisites (e.g., needing an operationId) or use cases (e.g., for debugging vs. general listing).
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 lists endpoints but doesn't describe how it behaves—such as whether it returns a paginated list, requires authentication, has rate limits, or what format the output takes. 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 that directly states the tool's purpose without unnecessary words. It's front-loaded and efficient, making it easy for an agent to parse quickly.
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 for a tool with parameters and sibling alternatives. It doesn't explain the return values, behavioral traits, or usage context, which are essential for an agent to invoke it correctly in a server with multiple related tools.
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 input schema has 100% description coverage, documenting both parameters ('tag' for filtering and 'limit' for result count with a default). The description doesn't add any meaning beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without compensating with extra details.
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 a specific verb ('List') and resource ('all available DigitalOcean API endpoints'), making it easy to understand what it does. However, it doesn't explicitly distinguish this tool from its sibling 'search_endpoints' or 'get_endpoint_details', 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 like 'search_endpoints' or 'get_endpoint_details'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
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 but offers minimal information. It doesn't mention whether this is a read-only operation, what permissions might be required, how results are returned (pagination, format), or any rate limits. The description is too sparse for a mutation-sensitive 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 wasted words. It's appropriately sized for a simple search tool and front-loads the core purpose immediately.
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, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what the search returns (e.g., endpoint names, IDs, metadata), how to interpret results, or when to prefer this over 'list_endpoints'. For a tool with contextual complexity, this is inadequate.
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 input schema has 100% description coverage, so the schema already documents both parameters ('query' and 'limit') adequately. The description doesn't add any additional meaning beyond what the schema provides, such as search syntax examples or result ordering, which keeps it at the baseline 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 verb ('Search for') and resource ('DigitalOcean API endpoints'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'list_endpoints' or 'get_endpoint_details', which would be needed for 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 like 'list_endpoints' or 'get_endpoint_details'. There's no mention of prerequisites, context, or exclusions, leaving the agent with insufficient information to choose between similar tools.
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 but only states the action without behavioral details. It doesn't disclose if this is a read-only operation, requires authentication, has rate limits, or what the return format might be, which is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to 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), the description is minimally adequate but lacks depth. Without annotations or output schema, it should ideally explain return values or behavioral context, but it only covers the basic action, leaving gaps in completeness.
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 with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, but since there are no parameters, this is acceptable, aligning with the baseline for 0 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 verb ('List') and resource ('endpoint tags'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_endpoints' or 'search_endpoints' that also list resources, missing explicit distinction.
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 'list_endpoints' or 'search_endpoints'. The description lacks context about prerequisites or exclusions, leaving usage ambiguous.
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. It discloses that credentials can be auto-configured from an environment variable, which is useful context about setup behavior. However, it lacks critical behavioral details: it doesn't specify whether this is a one-time configuration or persistent, what permissions are needed, if it validates the token, or what happens on success/failure. For a credential configuration 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 appropriately sized and front-loaded: a single sentence states the core purpose, and a second sentence adds valuable context about auto-configuration. Every sentence earns its place with no wasted words or 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?
Given the tool's moderate complexity (credential configuration), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers the basic purpose and a setup nuance, but lacks details on behavioral outcomes, error handling, or integration context. It's complete enough to understand what the tool does but not how it behaves in practice.
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 fully documents both parameters (token and baseUrl). The description adds no parameter-specific information beyond what's in the schema. It mentions the environment variable auto-configuration, which relates to the token parameter indirectly but doesn't explain parameter semantics. Baseline 3 is appropriate when the schema does all the work.
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: 'Configure DigitalOcean API credentials' specifies the verb (configure) and resource (API credentials). It distinguishes from siblings like call_digitalocean_api (which executes API calls) and list_endpoints (which retrieves data). However, it doesn't explicitly differentiate from potential credential-management siblings that might exist elsewhere.
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 provides implied usage guidance: it mentions auto-configuration from an environment variable, suggesting this tool is for manual credential setup when auto-configuration isn't available. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., when environment variables are preferred, or if there are other credential methods). No exclusions or clear alternatives are named.
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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