Google Calendar - No deletion

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
The tool set mixes calendar and email operations without clear boundaries, causing potential misselection. For example, 'list_emails' and 'search_emails' overlap in purpose for email retrieval, while calendar tools like 'create_event' and 'list_events' are distinct but the server's dual-domain focus creates confusion.
Naming Consistency3/5
Naming conventions are mixed, with some tools using verb_noun patterns like 'create_event' and others using noun_verb like 'meeting_suggestion'. While readable, the inconsistency in structure (e.g., 'modify_email' vs. 'send_email') deviates from a predictable pattern.
Tool Count3/5
With 7 tools, the count is reasonable, but it feels borderline due to the server's split focus on both Google Calendar and Gmail. This scope might be too broad for the limited tool set, leading to a thin coverage in each domain.
Completeness2/5
For the implied dual-domain purpose, there are significant gaps. In calendar, tools lack update or delete event operations, and in email, there's no way to delete emails or manage drafts. The server name suggests calendar focus, but email tools are included without full lifecycle coverage.
Average 2.8/5 across 7 of 7 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.
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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

Behavior1/5
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. 'Send a new email' implies a write/mutation operation, but it doesn't disclose any behavioral traits such as permissions required, rate limits, whether it's synchronous/asynchronous, error handling, or what happens on success/failure. This is a significant gap 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 extremely concise with just three words, front-loaded with the core action. There's zero waste or redundancy, making it efficient for quick understanding, though this brevity contributes to gaps in other dimensions.
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 sending an email (a mutation operation with potential side effects), no annotations, no output schema, and a minimal description, this is incomplete. The description doesn't address key contextual aspects like return values, error conditions, or integration with sibling tools, leaving the agent under-informed 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%, with all 5 parameters (to, subject, body, cc, bcc) well-documented in the input schema. The description adds no parameter semantics beyond what the schema provides, so it meets the baseline of 3 where the schema does the heavy lifting without compensating for any gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Send a new email' clearly states the action (send) and resource (email), but it's quite basic and doesn't differentiate from potential sibling tools like 'modify_email' or provide any nuance about what constitutes a 'new' email versus other email operations. It avoids tautology but lacks specificity beyond the obvious.
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 'modify_email' or 'search_emails', nor does it mention any prerequisites or context for sending emails. It's a standalone statement with no usage context, leaving the agent to infer when this is appropriate.
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 states this is a creation operation but doesn't mention permissions required, whether it sends invitations to attendees, error conditions, or what happens on success/failure. This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a straightforward creation tool and front-loads the essential information.
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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after creation (e.g., returns event ID, sends notifications), error handling, or behavioral nuances. The 100% schema coverage helps with parameters but doesn't compensate for the lack of operational 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 schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the structured fields, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and resource ('calendar event'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'modify_email' or 'meeting_suggestion' that might also involve calendar operations, preventing 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. Given sibling tools like 'list_events' and 'meeting_suggestion', there's no indication of whether this is for manual creation versus automated suggestions, or any prerequisites for use.
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 'recent emails' but doesn't specify what 'recent' means (e.g., time frame, ordering), whether it's read-only or has side effects, or any rate limits or authentication needs. This leaves significant gaps for an agent to understand 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.
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 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. It doesn't explain what 'recent' entails, the return format (e.g., list structure, fields), or how it differs from sibling tools like 'search_emails'. For a tool with 2 parameters and no structured behavioral hints, more context is needed.
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 ('maxResults' and 'query') with their types and default values. The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline for high coverage.
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') and resource ('recent emails from Gmail inbox'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'search_emails' or 'modify_email' beyond the basic verb, 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 like 'search_emails' or 'modify_email'. The description implies a default listing of recent emails but offers no context on prerequisites, exclusions, or specific use cases.
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 action but doesn't mention permissions required, rate limits, pagination behavior, or what 'upcoming' means (e.g., default time range). This leaves significant gaps for a read operation with no structured safety hints.
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 any wasted words. 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 the lack of annotations and output schema, the description is incomplete for a tool with three parameters and no safety hints. It doesn't explain what 'upcoming' entails, the return format, or error handling, leaving the agent with insufficient context to use the tool effectively beyond basic invocation.
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%, so the input schema fully documents the three parameters (maxResults, timeMax, timeMin) with their types and defaults. The description adds no additional parameter semantics beyond implying a time-based filter through 'upcoming', which aligns with the schema but doesn't provide extra value.
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 'List upcoming calendar events' clearly states the verb ('List') and resource ('upcoming calendar events'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'create_event' or 'meeting_suggestion' beyond the basic action, 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. With siblings like 'create_event' and 'meeting_suggestion' available, there's no mention of when to choose listing events over creating them or using the suggestion tool, leaving usage context unclear.
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 the 30-day constraint but doesn't explain what 'suggest' entails (e.g., how slots are determined, whether it considers existing events, authentication needs, rate limits, or what the output looks like). For a tool with 8 parameters and no annotation coverage, this leaves significant gaps.
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 gets straight to the point with zero wasted words. It's appropriately sized and front-loaded with the core functionality.
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 tool with 8 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the suggestion algorithm, how parameters interact, what the return format is, or error conditions. The 30-day mention provides some context but doesn't compensate for the missing behavioral and output information.
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 doesn't add any parameter-specific information beyond what's already in the schema (which has 100% coverage). It mentions 'within the next 30 days' which relates to the time scope but doesn't clarify how this interacts with parameters like 'daysToSearch'. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Suggest available meeting slots within the next 30 days' - a specific verb ('suggest') and resource ('meeting slots') with a time constraint. However, it doesn't differentiate from sibling tools like 'create_event' or 'list_events' which handle different calendar operations, so it doesn't fully distinguish itself.
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 sibling tools like 'create_event' for scheduling meetings or 'list_events' for viewing existing ones, nor does it specify prerequisites or contextual constraints for slot suggestion.
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 implies a mutation operation ('Modify') but doesn't specify permissions required, whether changes are reversible, rate limits, or error conditions. The mention of actions like 'archive' and 'trash' hints at destructive behavior, but this isn't explicitly stated, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Modify email labels') and lists specific actions without unnecessary words. Every part earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits (e.g., side effects, permissions), usage context, and expected outcomes, leaving the agent poorly equipped to handle this tool effectively in real-world 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 (id, addLabels, removeLabels). The description adds minimal value by implying label types (e.g., 'archive', 'trash') but doesn't provide syntax details or examples beyond what the schema offers. This meets the baseline for high schema coverage.
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 ('Modify email labels') and specifies the types of modifications possible (archive, trash, mark read/unread), which distinguishes it from sibling tools like 'send_email' or 'list_emails'. However, it doesn't explicitly mention that it operates on existing emails or differentiate from potential label-specific tools, keeping it from 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. It doesn't mention prerequisites (e.g., needing an email ID from 'list_emails' or 'search_emails'), exclusions, or comparisons to other tools like 'create_event' for related actions. This leaves the agent without context for tool selection.
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 'advanced query' but fails to detail key traits like whether this is a read-only operation, potential rate limits, authentication requirements, or what the output format entails. For a search tool with no annotation coverage, this is a significant gap in transparency.
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 gets straight to the point without unnecessary words. It's appropriately sized for a tool with two parameters, though it could be slightly more informative without sacrificing brevity. The structure is front-loaded but lacks elaboration.
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 (search functionality with two parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, performance, or output format, leaving the agent with insufficient context for reliable use. This is inadequate for a search tool without structured support.
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 clear documentation for both parameters ('maxResults' and 'query'), including defaults and examples. The description adds no additional semantic meaning beyond the schema, such as explaining query syntax further or contextualizing parameter interactions. Baseline 3 is appropriate as the schema handles 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 ('search') and resource ('emails'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'list_emails' or 'modify_email', which might offer overlapping functionality. The 'advanced query' modifier adds some specificity but remains somewhat vague about what makes it 'advanced'.
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_emails' or 'modify_email'. It lacks context about prerequisites, such as authentication needs or when advanced queries are preferable to simpler listing. This omission leaves the agent without clear usage boundaries.
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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