Ein leistungsstarker Open-Source Model Context Protocol (MCP)-Server, der speziell dafür entwickelt wurde, die Gemini CLI direkt mit Ihrem Linear-Workspace zu verbinden. Verwalten Sie Issues, Teams und Workflows nahtlos über natürliche Sprache, direkt von Ihrem Terminal aus.
🔍 Suche & Entdeckung: Finden Sie Issues in Ihrem gesamten Workspace direkt über die Gemini CLI.
📝 Issue-Verwaltung: Rufen Sie Issues ab, erstellen und aktualisieren Sie diese (Status, Priorität, Bearbeiter usw.) mühelos.
💬 Zusammenarbeit: Fügen Sie Kommentare zu Issues hinzu, ohne Ihr Terminal zu verlassen.
🏢 Workspace-Einblicke: Listen Sie Teams, Benutzer und Workflow-Status auf, um Gemini den vollständigen Kontext zu bieten.
⚡ Echtzeit-Integration: Basiert auf dem Model Context Protocol für eine sichere Kommunikation mit geringer Latenz.
Related MCP server: Linear MCP Server
Stellen Sie vor Beginn sicher, dass Sie Folgendes installiert haben:
Node.js: Version 18.0.0 oder höher.
Gemini CLI: Installiert und auf Ihrem Computer konfiguriert.
Linear-Konto: Mit Zugriff zum Generieren eines API-Schlüssels.
Dieser Server ist in TypeScript geschrieben. Da die Gemini CLI ein Standard-JavaScript-Executable benötigt, um MCP-Server auszuführen, müssen wir das Projekt zuerst herunterladen und "bauen".
Befolgen Sie diese 4 Schritte, um alles zu verbinden.
Klonen Sie das Repository auf Ihren Computer:
git clone https://github.com/tenx-studio/Linear-MCP.git
cd linear-mcp-open-sourceInstallieren Sie die erforderlichen Pakete:
npm installKompilieren Sie den Code:
npm run build(Warum das? Dieser Befehl übersetzt den TypeScript-Code in eine dist/index.js-Datei, die genau die Datei ist, die die Gemini CLI zur Kommunikation mit Linear verwendet).
Navigieren Sie in Ihrem Linear-Workspace zu Settings > API > Personal API keys.
Klicken Sie auf New API key und geben Sie ihm einen beschreibenden Namen (z. B. "Gemini CLI").
Kopieren Sie den Schlüssel (Sie werden ihn für den letzten Schritt benötigen).
Jetzt teilen wir der Gemini CLI mit, wo sie den gebauten Server findet, und stellen ihr sicher Ihren API-Schlüssel zur Verfügung. Dies tun Sie, indem Sie die Einstellungsdatei der Gemini CLI bearbeiten.
Suchen Sie Ihre Einstellungsdatei:
Öffnen Sie Ihren Datei-Explorer oder Ihr Terminal und suchen Sie den .gemini-Ordner im Home-Verzeichnis Ihres Benutzers:
Windows: %USERPROFILE%\.gemini\settings.json (z. B. C:\Users\IhrName\.gemini\settings.json)
Mac/Linux: ~/.gemini/settings.json
(Hinweis: Falls der .gemini-Ordner oder die settings.json-Datei noch nicht existieren, erstellen Sie diese einfach).
Fügen Sie die Konfiguration hinzu:
Öffnen Sie settings.json in einem beliebigen Texteditor (wie VS Code, Notepad oder TextEdit) und fügen Sie den folgenden mcp-Block ein. Falls Ihre Datei bereits Inhalt hat, fügen Sie einfach den Abschnitt "mcp" innerhalb der Hauptklammern {} hinzu:
{
"mcp": {
"servers": {
"linear": {
"command": "node",
"args": ["/ABSOLUTE/PATH/TO/linear-mcp-open-source/dist/index.js"],
"env": {
"LINEAR_API_KEY": "lin_api_your_key_here"
}
}
}
}
}⚠️ Wichtige Anpassungen:
args: Sie MÜSSEN /ABSOLUTE/PATH/TO/... durch den tatsächlichen, vollständigen Pfad auf Ihrem Computer ersetzen, unter dem Sie dieses Repository geklont haben. (z. B. "D:/Projects/linear-mcp-open-source/dist/index.js").
LINEAR_API_KEY: Ersetzen Sie lin_api_your_key_here durch den exakten API-Schlüssel, den Sie in Schritt 2 kopiert haben.
Schließen Sie Ihr Terminal und öffnen Sie es erneut, um Ihre Gemini CLI-Sitzung neu zu starten. Die Integration ist jetzt aktiv!
Versuchen Sie, Gemini CLI Dinge zu fragen wie:
"List my Linear teams." "Find the issue about the login bug." "Create a new high priority issue in the Engineering team to update the README."
Sie fragen sich vielleicht: "Muss ich jedes Mal npm start ausführen, wenn ich das verwenden möchte? Muss ich manuell eine Serververbindung einrichten?"
Nein! Sie müssen nichts weiter tun.
Das Hinzufügen des Konfigurationsblocks in Schritt 3 ist die einzige erforderliche Einrichtung. Hier ist genau das, was im Hintergrund passiert:
Zero-Touch-Start: Da Sie die Konfiguration zu Ihrer settings.json hinzugefügt haben, fungiert die Gemini CLI nativ als Host.
Hintergrundausführung: Jedes Mal, wenn Sie einen neuen Gemini CLI-Chat starten, startet dieser automatisch diesen Linear MCP-Server in einem versteckten Hintergrundprozess.
Sicherer Handshake: Die Gemini CLI liest den "LINEAR_API_KEY" aus Ihren Einstellungen und injiziert ihn sicher direkt in den Server.
Sauberes Herunterfahren: Wenn Sie die Gemini CLI schließen, wird der Hintergrundserver automatisch beendet.
Sie konfigurieren es nur einmal in Ihren Einstellungen, und das Model Context Protocol übernimmt den gesamten Lebenszyklus automatisch für Sie!
Beiträge sind das, was die Open-Source-Community zu einem so erstaunlichen Ort zum Lernen, Inspirieren und Erschaffen macht. Jeder Beitrag, den Sie leisten, wird sehr geschätzt.
Forken Sie das Projekt
Erstellen Sie Ihren Feature-Branch (git checkout -b feature/AmazingFeature)
Committen Sie Ihre Änderungen (git commit -m 'Add some AmazingFeature')
Pushen Sie in den Branch (git push origin feature/AmazingFeature)
Öffnen Sie einen Pull Request
Dieses Projekt ist unter der ISC-Lizenz lizenziert.
Mit ❤️ für die KI-Entwickler-Community gebaut.
Add a comment to an issue
| Name | Required | Description | Default |
|---|---|---|---|
| issueId | Yes | The ID or key of the issue | |
| body | Yes | The content of the comment |
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 this is a write operation ('Add'), implying mutation, but doesn't cover important aspects like required permissions, whether the action is reversible, rate limits, 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.
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 tool and front-loads the essential information without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 doesn't address behavioral aspects like permissions, side effects, or response format, which are critical for an agent to use this tool correctly in context with its siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters (issueId and body) clearly documented in the schema. The description doesn't add any meaningful semantic context beyond what the schema already provides (e.g., format examples, constraints), 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add a comment') and the target resource ('to an issue'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from its siblings (like linear_update_issue which might also handle comments), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 existing issue), exclusions, or how it differs from sibling tools like linear_update_issue that might also modify issues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new issue in a team
| Name | Required | Description | Default |
|---|---|---|---|
| teamId | Yes | The ID of the team to create the issue in | |
| title | Yes | The title of the issue | |
| description | No | The description of the issue |
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 creates a new issue, implying a write operation, but doesn't mention permission requirements, whether the creation is reversible, rate limits, 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.
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 for a basic 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool (creating issues) with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after creation—whether it returns the new issue ID, success status, or error details. For a tool that modifies system state, more behavioral context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for all three parameters (teamId, title, description). The description doesn't add any parameter-specific information beyond what the schema already provides, such as format examples or constraints. 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.
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 issue') and the resource ('in a team'), making the purpose immediately understandable. However, it doesn't differentiate this from sibling tools like linear_update_issue or linear_search_issues, which would require more specific language about creation versus modification or retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 like needing teamId from linear_get_teams, nor does it contrast with linear_update_issue for modifying existing issues or linear_search_issues for finding issues. 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.
Get issues from a specific team
| Name | Required | Description | Default |
|---|---|---|---|
| teamKey | No | The key of the team (e.g., ENG, DES) | |
| limit | No | Maximum number of issues to return |
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 basic action without disclosing behavioral traits. It doesn't mention permissions needed, rate limits, pagination, sorting, what fields are returned, or whether it's read-only (implied but not explicit). This leaves significant gaps for agent understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 purpose, though it could be more structured with additional context. It earns high marks for brevity but loses a point for under-specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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-heavy sibling set (e.g., linear_create_issue, linear_update_issue), the description is incomplete. It doesn't address key contextual aspects like return format, error handling, or how this read tool fits among write operations, leaving the agent with insufficient information for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 (teamKey and limit) adequately. The description adds no additional meaning beyond what the schema provides, such as examples of team keys or context for limit usage, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Get issues from a specific team' which provides a clear verb ('Get') and resource ('issues'), but it's vague about scope and doesn't distinguish from sibling tools like 'linear_search_issues' or 'linear_get_teams'. It doesn't specify whether this retrieves all issues, recent issues, or filtered issues beyond team.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'linear_search_issues' or 'linear_get_teams'. The description implies team-based retrieval but doesn't mention prerequisites, exclusions, or comparative contexts with other issue-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all teams in the workspace
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
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 it's a list operation, implying read-only behavior, but doesn't cover aspects like pagination (implied by the 'limit' parameter in schema), rate limits, authentication needs, or what the output looks like. This is a significant gap for a tool with undocumented parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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, making it efficient and easy to parse, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema, no annotations), the description is incomplete. It lacks parameter details, usage context, and output information, making it inadequate for an agent to fully understand how to invoke and interpret results from this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 1 parameter with 0% description coverage, and the tool description provides no information about parameters. It doesn't mention the 'limit' parameter at all, leaving it completely undocumented. This fails to compensate for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('all teams in the workspace'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like linear_get_users or linear_get_workflow_states, which follow the same 'List all X' pattern, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, context for team selection, or comparison to other tools like linear_get_users for user-related queries, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all users in the workspace
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
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 states the action ('List all users') but doesn't describe return format (e.g., pagination, fields included), error conditions, rate limits, or authentication requirements. For a tool with zero annotation coverage, this is insufficient to inform the agent about how the tool behaves beyond its basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core action without unnecessary words. It is front-loaded with the essential information ('List all users in the workspace'), making it easy for an agent to parse quickly. There is no wasted language or redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema, no annotations), the description is incomplete. It lacks details on return values (e.g., user object structure), error handling, and practical usage context. While concise, it doesn't provide enough information for an agent to confidently invoke the tool without additional assumptions or trial-and-error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter ('limit') with 0% description coverage, and the tool description provides no information about parameters. The description doesn't mention the 'limit' parameter, its purpose (e.g., pagination control), or default value, leaving the agent to infer usage from the schema alone. With low schema coverage, the description fails to compensate by explaining parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('all users in the workspace'), making the purpose immediately understandable. It distinguishes this from sibling tools like linear_create_issue or linear_update_issue by focusing on user retrieval rather than issue management. However, it doesn't explicitly differentiate from linear_get_teams or linear_get_workflow_states in terms of resource type specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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., authentication needs), compare it to sibling tools like linear_search_issues for filtered queries, or indicate scenarios where listing all users is appropriate. Without annotations or context signals about usage, this leaves the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get workflow states for a specific team
| Name | Required | Description | Default |
|---|---|---|---|
| teamId | Yes | The ID of the team |
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 but offers minimal information. It implies a read operation ('Get'), but doesn't specify permissions required, rate limits, pagination, error handling, or what the return format looks like (e.g., list of states with properties). This is inadequate 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 workflow states are, their structure, or how they relate to issues or teams. For a tool that likely returns structured data (states with names, IDs, colors), this leaves significant gaps for an agent to understand the tool's full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'teamId' clearly documented in the schema. The description adds no additional parameter semantics beyond implying it's for a 'specific team,' which the schema already covers. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('workflow states for a specific team'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like linear_get_issues or linear_get_teams, which also retrieve data but for different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 a team ID), exclusions, or comparisons to siblings like linear_get_issues (which might include state information). This leaves the agent with minimal 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.
Search for issues using a query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query | |
| limit | No | Maximum number of results to return |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but discloses almost nothing beyond the basic action. It doesn't mention whether this is a read-only operation, what permissions are needed, how results are returned (format, pagination), rate limits, or error conditions. 'Search' implies read-only, but this isn't explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a simple search tool and front-loads the core action. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., issue objects, IDs), how results are structured, or any behavioral nuances. The agent lacks critical context to use this tool effectively beyond basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both parameters (query and limit). The description adds no additional meaning about parameters beyond what's in the schema. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Search for issues using a query' clearly states the verb (search) and resource (issues), but it's vague about scope and doesn't differentiate from sibling tools like 'linear_get_issues'. It provides basic purpose but lacks specificity about what kind of search this is (e.g., full-text, filtered, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'linear_get_issues' or other siblings. There's no mention of prerequisites, context, or exclusions. The agent must 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.
Update an existing issue
| Name | Required | Description | Default |
|---|---|---|---|
| issueId | Yes | The ID or key (e.g., TEN-1) of the issue | |
| title | No | ||
| description | No | ||
| stateId | No | The ID of the workflow state (e.g., In Progress) | |
| priority | No | Priority (0-4) | |
| assigneeId | No | The ID of the user to assign the issue to |
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. 'Update an existing issue' implies a mutation operation but doesn't specify what permissions are required, whether changes are reversible, what happens to fields not mentioned, or what the response looks like. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just three words, with zero wasted language. It's front-loaded with the essential action and resource, making it immediately understandable despite its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what happens during updates, what fields can be modified, error conditions, or return values. The combination of complexity and lack of structured support requires more descriptive content than provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 67% (4 of 6 parameters have descriptions), which is moderate. The description itself adds no parameter information beyond what's in the schema. With schema doing most of the work, the baseline score of 3 is appropriate, though the description doesn't compensate for the 33% coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update an existing issue' clearly states the verb (update) and resource (issue), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential alternatives like linear_create_issue or linear_search_issues, which would require more specific scope information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (like needing an existing issue ID), when not to use it, or how it differs from sibling tools like linear_create_issue for new issues or linear_search_issues for finding issues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0linear_add_commentlinear_create_issuelinear_get_issueslinear_get_teamslinear_get_userslinear_get_workflow_stateslinear_search_issueslinear_update_issueScored across 8 tools
Each tool has a clearly distinct purpose targeting specific resources and actions in the Linear domain, such as creating issues, getting issues, adding comments, updating issues, searching issues, and retrieving teams, users, and workflow states. There is no ambiguity or overlap in functionality, making it easy for an agent to select the correct tool.
All tool names follow a consistent 'linear_verb_noun' pattern with snake_case, such as linear_create_issue, linear_get_issues, and linear_update_issue. This predictability enhances readability and usability for agents, with no deviations in naming conventions.
With 8 tools, the server is well-scoped for managing issues and related resources in Linear, covering core operations like CRUD for issues, commenting, searching, and retrieving teams, users, and states. Each tool earns its place without feeling excessive or insufficient for the domain.
The tool set provides strong coverage for issue management, including create, get, update, comment, and search, along with essential context tools for teams, users, and workflow states. A minor gap is the lack of delete operations for issues or other resources, but agents can work around this with updates or other methods.
MCP server for Linear project management and issue tracking
Search, read and create Linear issues, projects, teams and cycles.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Linear MCP — wraps the Linear GraphQL API (OAuth)
