Google Jules MCP

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. Tools like jules_analyze_code, jules_approve_plan, jules_create_task, and jules_get_task each target specific actions on different aspects of the Jules system, and there is no overlap in functionality that would cause confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with the prefix 'jules_' and snake_case throughout. Examples include jules_create_task, jules_list_tasks, and jules_send_message, making the naming predictable and easy to understand.
Tool Count5/5
With 13 tools, the set is well-scoped for managing Google Jules tasks, covering creation, retrieval, control, and session management. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness5/5
The tool surface provides complete coverage for the Jules task lifecycle, including create, get, list, approve, resume, and send messages, along with session setup and authentication tools. There are no obvious gaps that would hinder agent workflows.
Average 3/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes code changes and diff, but does not explain what 'analyze' entails (e.g., returns insights, identifies issues, or provides summaries), whether it requires specific permissions, or any side effects like rate limits. 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.
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 front-loaded and wastes no space, 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. It does not explain what the analysis returns (e.g., a report, insights, or structured data), how it handles errors, or any behavioral traits. For a tool with two parameters and no structured output information, this leaves the agent with insufficient context 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 100%, with clear descriptions for both parameters ('taskId' and 'includeSourceCode'), so the schema provides adequate parameter semantics. The description does not add any additional meaning beyond what the schema already covers, such as explaining how 'taskId' relates to Jules tasks or the implications of including source code, resulting in a 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 action ('Analyze') and the target ('code changes and diff in a Jules task'), which is specific and actionable. However, it does not explicitly differentiate this tool from siblings like 'jules_get_task' or 'jules_list_tasks', which might also involve task-related operations, leaving some ambiguity about its unique role.
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 when to analyze code changes instead of retrieving task details with 'jules_get_task'. There is no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based on 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. It implies a mutation ('Approve'), but doesn't specify permissions needed, side effects (e.g., if approval triggers execution), or error conditions. This leaves significant gaps for a tool that likely changes task states.
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 no wasted words. It's front-loaded with the core action and resource, 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 complexity of a mutation tool (approving plans likely changes task states) with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects like what happens post-approval, return values, or error handling, which are crucial for effective tool 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 input schema already documents the 'taskId' parameter. The description doesn't add any meaning beyond this, such as clarifying what constitutes a valid task ID or URL format. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.
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 ('Approve') and the resource ('Jules execution plan for a task'), making the purpose understandable. However, it doesn't differentiate from siblings like 'jules_resume_task' or 'jules_get_task', which might involve similar task operations but different actions.
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. For example, it doesn't specify if this should be used after analysis or before execution, or how it differs from 'jules_resume_task' in handling task states.
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 disclosing behavioral traits such as permissions needed, rate limits, whether tasks are created synchronously/asynchronously, or error handling for partial failures. It mentions the source data but not the operational impact.
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 resource without any wasted words. It is appropriately sized for the tool's purpose, 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 complexity of a bulk creation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects, error handling, and what the tool returns, leaving gaps that could hinder an AI agent's ability 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?
The schema description coverage is 100%, with the 'tasks' parameter fully documented in the schema. The description adds minimal value by hinting at the content ('list of descriptions and repositories'), but doesn't provide additional semantics beyond what the schema already specifies, 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 ('Create multiple tasks') and the resource ('tasks'), specifying they come from 'a list of descriptions and repositories'. It distinguishes from the sibling 'jules_create_task' by indicating bulk creation, though it doesn't explicitly name that sibling for comparison.
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 'jules_create_task' for single tasks or other task-related tools. The description implies bulk operations but lacks explicit context, prerequisites, or exclusions for usage.
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. It states 'Create a new task' which implies a write/mutation operation, but doesn't disclose behavioral traits like authentication needs, rate limits, side effects, or what happens on success/failure. For a mutation tool with zero 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 front-loads the core action. It wastes no words, though it could be slightly more structured (e.g., by explicitly listing parameters). Every part earns its place, making it concise and clear.
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 with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, response format, or error handling, which are critical for an AI agent to use it correctly. The schema handles parameters well, but overall context is lacking.
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 fully documents all three parameters (branch, description, repository) with their purposes and optionality. The description mentions 'repository and description' but adds no meaning beyond what the schema provides, such as format details or usage examples. Baseline 3 is appropriate when 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 action ('Create a new task') and target resource ('in Google Jules'), specifying the required elements ('with repository and description'). It distinguishes from siblings like 'jules_list_tasks' (read) and 'jules_bulk_create_tasks' (multiple), though not explicitly. However, it doesn't fully differentiate from 'jules_resume_task' (which might also involve task creation).
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 explicit guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'jules_bulk_create_tasks' for multiple tasks, 'jules_resume_task' for existing tasks, or other siblings. The description implies usage for single-task creation but lacks context about prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't describe what those details include, whether it's a read-only operation, error handling for invalid IDs, or any performance considerations. 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.
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 function without any unnecessary words. It is front-loaded with the core action and resource, 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 details are returned, how errors are handled, or any dependencies on other tools. For a tool that likely returns structured task data, this omission reduces its usefulness 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 schema description coverage is 100%, with the parameter 'taskId' fully documented in the schema as 'Task ID or full Jules task URL'. The description adds no additional semantic context beyond what the schema provides, such as examples or format specifics, 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 ('Get details') and resource ('a specific Jules task'), making the purpose immediately understandable. It distinguishes from siblings like 'jules_list_tasks' by focusing on a single task rather than listing multiple. However, it doesn't specify what details are retrieved, 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 'jules_list_tasks' for browsing or 'jules_create_task' for new tasks. It mentions the parameter ('by ID or URL') but doesn't explain scenarios where this tool is preferred over others in the sibling set.
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 'Resume' implies a state change (from paused to active), the description doesn't specify what happens upon resumption (e.g., does execution continue immediately?), potential side effects, authentication needs, or error conditions. 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 with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place by conveying essential information without redundancy.
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 incomplete. It lacks details on behavioral outcomes (e.g., what the tool returns, error handling), prerequisites (e.g., task state), and how it fits within the broader task lifecycle. Given the complexity implied by sibling tools, 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?
The schema description coverage is 100%, with the single parameter 'taskId' documented as 'Task ID or URL'. The description adds no additional semantic context beyond what's in the schema (e.g., it doesn't clarify format examples or where to find the ID). With high schema coverage, the baseline score 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 action ('Resume') and the resource ('a paused Jules task'), making the purpose immediately understandable. It doesn't explicitly differentiate from siblings like 'jules_get_task' or 'jules_list_tasks', but the verb 'Resume' implies a specific state transition that distinguishes it from those read-only operations.
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., that the task must be paused), exclusions, or comparisons to siblings like 'jules_create_task' or 'jules_bulk_create_tasks'. The agent must infer usage from the name and context 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?
No annotations are provided, so the description carries full burden. It mentions the tool captures a screenshot but lacks behavioral details: it doesn't specify if this requires permissions, affects page state, has rate limits, or what the output looks like (e.g., file format, storage location). For a tool with potential side effects (navigation via taskId), this is inadequate transparency.
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 action ('Take a screenshot') and purpose. Every word earns its place with no redundancy or fluff, making it highly concise and well-structured for quick understanding.
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, no output schema, and a tool that involves potential navigation (via taskId) and file creation, the description is incomplete. It doesn't cover behavioral aspects like side effects, output format, or error handling. For a tool with 2 parameters and debugging use, more context is needed to ensure 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 (filename and taskId) as optional strings. The description adds no additional meaning beyond the schema—it doesn't explain parameter interactions (e.g., if taskId affects the screenshot) or usage nuances. 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 action ('Take a screenshot') and target ('current Jules page'), with a specific purpose ('for debugging and verification'). It distinguishes from siblings like jules_analyze_code or jules_create_task by focusing on visual capture rather than code analysis or task management. However, it doesn't explicitly differentiate from potential similar tools (none exist in the sibling list), keeping it at 4 rather than 5.
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 minimal guidance: it implies use for debugging/verification but offers no explicit when-to-use rules, prerequisites, or alternatives. For example, it doesn't specify if this should be used instead of other debugging tools or under what conditions (e.g., after errors). With no context on exclusions or comparisons, this is a basic level of guidance.
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 states the tool sends a message/instruction but doesn't disclose behavioral traits like whether this is a synchronous/asynchronous operation, what happens if the task isn't active, permission requirements, rate limits, or expected response format. 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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every part contributing 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?
Given no annotations, no output schema, and a mutation tool (sending messages implies writing), the description is incomplete. It lacks crucial context about how the tool behaves, what it returns, error conditions, or dependencies on other tools (e.g., requiring an active task from 'jules_create_task' or 'jules_resume_task').
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 both parameters ('message' and 'taskId') clearly documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating 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 clearly states the action ('Send a message/instruction') and target ('to Jules in an active task'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'jules_resume_task' or 'jules_approve_plan', which might also involve communication with Jules in task contexts.
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 implies usage is for 'an active task', but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'jules_approve_plan' for approvals or 'jules_resume_task' for task resumption). There's no mention of prerequisites, exclusions, or comparison to sibling 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, the description carries full burden but only states the basic action. It lacks details on behavioral traits such as whether this persists cookies across sessions, requires specific browser state, has side effects, or handles errors, which are critical for a tool involving authentication.
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, clearly front-loaded with the core action and purpose. Every word earns its place without redundancy.
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 authentication and no annotations or output schema, the description is incomplete. It doesn't explain what happens after setting cookies (e.g., session persistence, return values, or error handling), leaving significant gaps for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters. The description adds minimal value by mentioning 'string or JSON' for cookies and implying authentication context, but doesn't provide additional semantics beyond what the schema already covers.
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 ('Set browser cookies') and purpose ('for authentication'), which is specific and actionable. However, it doesn't distinguish this tool from its sibling 'jules_get_cookies' beyond the direction of operation, missing explicit differentiation.
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. It mentions authentication but doesn't specify prerequisites, timing, or contrast with other tools like 'jules_session_info' or 'jules_setup_wizard' that might relate to session management.
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 offers minimal behavioral insight. It mentions 'interactive session' and 'automated...configuration' but doesn't disclose critical traits like whether this initiates authentication flows, requires user interaction, handles errors, or affects existing sessions. For a setup tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('interactive session setup wizard') without unnecessary elaboration. Every word contributes directly to understanding the tool's function, making it appropriately sized for its complexity.
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 setup/configuration tool with no annotations, no output schema, and incomplete parameter documentation, the description is inadequate. It doesn't explain what the tool returns, how it interacts with users or systems, or what happens after configuration. Given the complexity implied by 'interactive' and 'automated', more context is needed for 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 50%, with the 'environment' parameter well-documented but the 'preferences' object lacking descriptions for its nested properties. The description adds no parameter-specific information beyond what's in the schema, failing to compensate for the coverage gap. However, with only 2 parameters total, the baseline remains moderate.
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 as an 'interactive session setup wizard for automated Google authentication configuration' with specific verbs ('setup wizard', 'configuration') and resource ('Google authentication'). It distinguishes from siblings like jules_get_cookies or jules_session_info by focusing on setup/configuration rather than retrieval or information display.
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., when authentication is needed), exclusions, or comparisons with sibling tools like jules_set_cookies or jules_session_info. The agent must infer usage from the purpose 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?
No annotations are provided, so the description carries the full burden. It states it lists tasks with status, but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or what 'all' entails (e.g., all accessible tasks). For a list tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 action ('List all Jules tasks with their status'). There is zero waste, and it directly communicates the tool's function without unnecessary details.
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 low complexity (list operation with 2 optional parameters), 100% schema coverage, and no output schema, the description is minimally adequate. However, it lacks context on behavioral aspects (e.g., default behavior, error handling) and doesn't compensate for the absence of annotations, leaving room for improvement.
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 fully documents the parameters (limit and status with enum). The description adds no additional meaning beyond implying filtering by status, which is already covered. 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.
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 ('Jules tasks'), specifying the scope as 'all' with their status. It distinguishes from siblings like jules_get_task (single task) and jules_bulk_create_tasks (creation), but doesn't explicitly differentiate from other list-like tools if they existed. The purpose is specific and actionable.
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. It doesn't mention prerequisites, context for filtering, or compare to siblings like jules_get_task for single tasks or jules_session_info for session-related data. The agent must infer usage from the name and schema 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?
No annotations are provided, so the description carries the full burden. It mentions the tool's purpose but lacks behavioral details such as permissions needed, whether it accesses sensitive data, rate limits, or what the output looks like. 'Extract' suggests a read operation, but without annotations, safety and behavioral context are insufficiently disclosed.
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 states the purpose clearly without redundancy. It is appropriately sized and front-loaded, 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?
Given no annotations and no output schema, the description is incomplete. It explains the tool's purpose but lacks details on behavior, output format, or error handling. For a tool that likely handles sensitive data (cookies), more context is needed to ensure safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with one parameter ('format') fully documented in the schema. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without compensating 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 clearly states the action ('Extract') and resource ('current browser cookies'), with a specific purpose ('for session persistence and backup'). It distinguishes from sibling 'jules_set_cookies' by being a read operation versus a write operation, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('session persistence and backup') but doesn't provide explicit guidance on when to use this tool versus alternatives like 'jules_session_info' or 'jules_set_cookies'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'authentication status,' which hints at read-only behavior, but doesn't clarify if this tool requires authentication itself, what data it returns (e.g., format, scope), or any side effects (e.g., rate limits, session updates). For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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, clear sentence with zero waste. It's front-loaded with the core purpose ('Get current session configuration and authentication status'), and every word earns its place by specifying what is retrieved. No unnecessary details or redundancy are present.
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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on return values, authentication requirements, or when to use it. Without annotations or output schema, more context on behavior and output would be helpful, but it meets the basic threshold for a simple 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?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately avoids mentioning any. Baseline for 0 parameters is 4, as it doesn't mislead or omit necessary param info.
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 specific verbs ('Get') and resources ('current session configuration and authentication status'). It distinguishes itself from siblings like jules_get_cookies or jules_set_cookies by focusing on session configuration rather than cookie management. However, it doesn't explicitly differentiate from all siblings (e.g., jules_setup_wizard might relate to session setup).
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 authentication is required to call it), when it's most useful (e.g., during session initialization or debugging), or what alternatives exist among siblings like jules_get_cookies for authentication details. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![google-jules-mcp MCP server](https://glama.ai/mcp/servers/mberjans/google-jules-mcp/badges/card.svg)](https://glama.ai/mcp/servers/mberjans/google-jules-mcp)

Score Badge

Copy to your README.md:

[![google-jules-mcp MCP server](https://glama.ai/mcp/servers/mberjans/google-jules-mcp/badges/score.svg)](https://glama.ai/mcp/servers/mberjans/google-jules-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mberjans/google-jules-mcp'

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