MCP Linear App

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 targeting specific resources and actions in Linear. The tools are well-differentiated by resource type (issue, comment, profile, team) and operation (create, get, search, update), with no overlapping functionality that could cause confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case throughout. The naming convention is perfectly predictable with verbs like create, get, search, and update consistently applied to appropriate nouns like issue, comment, profile, and team.
Tool Count5/5
With 9 tools, this server is well-scoped for issue management in Linear. Each tool earns its place by covering essential operations without being overwhelming, providing a balanced set that supports core workflows without unnecessary complexity.
Completeness4/5
The tool set provides excellent CRUD coverage for issues and comments, plus profile and team lookup capabilities. The only minor gap is the lack of a delete_issue tool, though update_issue might handle deletion, and there's no direct tool for listing all issues without search parameters, but agents can work around these limitations effectively.
Average 2.9/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the action ('gets an issue') without disclosing behavioral traits like whether it's a read-only operation, authentication requirements, error handling, or rate limits. For a tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, directly stating the tool's function without unnecessary 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 complexity (a read operation with one parameter) and lack of annotations/output schema, the description is incomplete. It doesn't explain what 'gets' entails (e.g., returns issue details), error cases, or how it differs from siblings. For a tool with no structured data support, 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%, with the parameter 'issueId' documented as 'The ID of the issue to retrieve'. The description adds no additional meaning beyond what the schema provides. According to rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.
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 states the tool 'gets an issue from Linear', which provides a basic verb+resource combination. However, it's vague about what 'gets' means (retrieves, fetches, reads) and doesn't distinguish it from sibling tools like 'search_issues' or 'get_comment'. The purpose is understandable but lacks specificity.
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 when to use 'get_issue' versus 'search_issues' for finding issues, or prerequisites like needing an issue ID. There's no explicit or implied context for usage decisions.
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 'creates a comment', implying a write operation, but doesn't address permissions, side effects, rate limits, or response format. This is a significant gap for a mutation tool, making it inadequate for safe usage.
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, making it easy to parse quickly, with no wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a write operation with no annotations and no output schema, the description is incomplete. It lacks behavioral details (e.g., permissions, response), usage context, and output information, failing to provide enough guidance for reliable agent 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%, with clear descriptions for both parameters ('comment' and 'issueId'). The description doesn't add any semantic details beyond what the schema provides, such as format constraints or examples, so it meets the baseline of 3 without compensating or enhancing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('creates a comment') and target resource ('on an issue in Linear'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'update_comment' or 'get_comment', missing an opportunity for sibling differentiation that would warrant a score of 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 no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., needing an existing issue), exclusions, or comparisons to siblings like 'update_comment' for editing or 'get_comment' for reading, leaving the agent without usage context.
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 tool, implying mutation, but doesn't mention authentication requirements, rate limits, error conditions, or what happens on success (e.g., returns issue ID). For a mutation tool with zero annotation coverage, this is insufficient.
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 core purpose without unnecessary words. It's appropriately sized and front-loaded with 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 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what happens after creation (e.g., returns issue object/ID), error handling, or behavioral constraints, leaving significant gaps for agent understanding.
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 7 parameters. The description adds no parameter information beyond what's in the schema, meeting the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('creates') and resource ('an issue in Linear'), making the purpose immediately understandable. It doesn't distinguish from siblings like 'update_issue' or 'search_issues', but the verb 'creates' is specific enough for basic 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?
The description provides no guidance on when to use this tool versus alternatives like 'update_issue' or 'search_issues'. It doesn't mention prerequisites (e.g., needing a teamId) or appropriate contexts for creating versus updating issues.
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 but offers minimal information. It doesn't specify whether this is a read-only operation, what permissions are required, how comments are returned (e.g., format, pagination), or any rate limits. The phrase 'gets comments' implies retrieval but lacks operational details needed for safe use.
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, straightforward sentence that efficiently conveys the core function without unnecessary words. However, it could be more front-loaded with critical details like behavioral traits or usage context, which would improve its structure for agent decision-making.
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 that likely returns structured comment data. It doesn't explain what the output contains (e.g., comment text, authors, timestamps) or address potential complexities like error handling or authentication needs, leaving significant gaps for an AI agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'issueId' clearly documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting without compensating for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('gets') and resource ('comments from an issue in Linear'), making it immediately understandable. However, it doesn't distinguish this tool from its sibling 'get_issue' or explain what differentiates getting comments from getting the issue itself, 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 'get_issue' and 'search_issues' available, there's no indication whether this tool retrieves all comments for a specific issue or if there are filtering options, nor when one might choose this over other comment-related tools like 'create_comment' or 'update_comment'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic action without mentioning critical details like whether this is a read-only operation, if it requires authentication, potential rate limits, or what the return format looks like. For a search tool with multiple parameters, 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 extremely concise with a single sentence that directly states the tool's function without any unnecessary words. It's front-loaded with the core purpose and wastes no space on redundant information, making it efficient 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 the tool has 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the search returns (e.g., issue objects with fields), how results are ordered, or any behavioral constraints. For a search operation in a system like Linear, more context about the operation's scope and results 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 all parameters well-documented in the schema itself (e.g., 'keyword' filters by title/description, 'priority' and 'status' have enums, 'limit' and 'skip' have defaults). The description adds no additional parameter information beyond what's already in the schema, meeting the baseline expectation but not providing 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 clearly states the tool's purpose as 'searches for issues in Linear' with a specific verb ('searches') and resource ('issues'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'get_issue' which might retrieve a single issue, leaving some ambiguity about when to use this versus other issue-related tools.
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 'get_issue' or 'create_issue'. It lacks any context about appropriate scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and parameters.
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 it states the tool can update or delete a comment, it lacks critical details: it doesn't specify permissions required, whether updates are reversible, rate limits, error conditions (e.g., invalid commentId), or what happens on deletion (e.g., permanent vs. soft delete). 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 purpose. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., by separating update and delete scenarios). Every part of the sentence earns its place by conveying 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 the tool's complexity (a mutation operation with three parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, side effects, or error handling, nor does it explain return values. For a tool that modifies or deletes data, this leaves the agent with insufficient context to use it safely and 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 all three parameters (commentId, comment, delete) well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides—it doesn't explain parameter interactions (e.g., how 'delete' overrides 'comment'), formats, or constraints. 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: 'updates or deletes an existing comment on an issue in Linear'. It specifies the verb (update/delete), resource (comment), and context (issue in Linear), which is specific and actionable. However, it doesn't explicitly distinguish this tool from its sibling 'create_comment', which handles comment creation rather than modification/deletion.
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 existing comment ID), when not to use it (e.g., for creating new comments), or refer to sibling tools like 'create_comment' or 'get_comment'. Usage is implied by the purpose statement but not explicitly articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'updates an issue' which implies a mutation operation, but it doesn't disclose any behavioral traits such as required permissions, whether updates are 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.
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 with zero waste. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place, and there's no unnecessary 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 complexity (a mutation tool with 9 parameters, no annotations, and no output schema), the description is incomplete. It doesn't address behavioral aspects like permissions or side effects, provide usage context, or explain return values. For a tool that modifies data in a system like Linear, this leaves significant gaps that could hinder correct agent 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%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or provide examples). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.
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 'updates an issue in Linear' with a specific verb ('updates') and resource ('issue in Linear'). It distinguishes from siblings like 'create_issue' (creation vs. update) and 'search_issues' (search vs. update), though it doesn't explicitly mention all siblings. The purpose is clear but could be more specific about what aspects can be updated.
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 existing issue ID), when to choose 'update_issue' over 'create_issue' or 'update_comment', or any constraints like permissions. Usage is implied by the name but not explicitly stated, leaving the agent to infer from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves the current user's profile but doesn't cover aspects like authentication requirements, rate limits, error handling, or the response format. This leaves significant gaps in understanding how the tool behaves in practice.
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 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 for a tool that likely involves authentication and returns user data. It doesn't explain what the profile contains, how errors are handled, or any dependencies, leaving the agent with insufficient context to use the tool effectively.
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%, meaning there are no parameters to document. The description doesn't need to add parameter semantics, so a baseline of 4 is appropriate, as it avoids redundancy while clearly indicating no inputs are required.
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 ('gets') and resource ('current user's profile from Linear'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_team_id' or 'get_issue', which also retrieve data from Linear but target different resources, so it misses the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as authentication, or compare it to sibling tools like 'get_team_id' for team-related data, leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool 'gets' data, implying a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns paginated results, or what format the output takes. For a 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.
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 without unnecessary words. Every part of the sentence earns its place by specifying what is retrieved, from where, and the resource type, 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 tool's simplicity (0 parameters, no output schema), the description is minimal but incomplete. It lacks details on behavioral aspects like authentication needs, output format, or error handling. Without annotations or an output schema, the description should provide more context to fully guide an AI agent, but it doesn't compensate for these gaps.
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's no need for parameter documentation in the description. The baseline for 0 parameters is 4, as the description appropriately doesn't waste space on non-existent parameters, though it could briefly note the lack of inputs for clarity.
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: 'gets all teams and their IDs from Linear.' It specifies the verb ('gets'), resource ('teams and their IDs'), and data source ('Linear'). However, it doesn't explicitly differentiate from sibling tools like get_profile or search_issues, 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. It doesn't mention prerequisites, timing, or comparisons to siblings like get_profile (which might return user-specific data) or search_issues (which might filter issues by team). This lack of contextual direction limits its utility for an AI agent.
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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