MCP Server for Slack

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. For example, lookup_user_by_email targets user identification, slack_get_channel_history retrieves channel messages, and slack_post_message handles message creation, ensuring agents can easily differentiate between them.
Naming Consistency4/5
The naming is mostly consistent with a 'slack_' prefix and descriptive verb_noun patterns, but there is one deviation: lookup_user_by_email lacks the 'slack_' prefix, which slightly breaks the pattern while remaining readable.
Tool Count5/5
With 9 tools, the count is well-scoped for a Slack integration server. It covers core functionalities like user management, messaging, and channel operations without being excessive or insufficient for the domain.
Completeness4/5
The tool set provides comprehensive coverage for common Slack workflows, including user lookup, messaging, and channel interactions. A minor gap exists in lacking tools for updating or deleting messages, but agents can still perform most essential tasks effectively.
Average 3/5 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
- Last stable release on March 11, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
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.
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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.
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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 the full burden of behavioral disclosure. It states the action ('lookup') but doesn't describe what the tool returns (e.g., user details, existence check), error handling (e.g., for invalid emails), permissions required, or rate limits. For a lookup 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 directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to understand at a glance. Every part of the sentence earns its place by specifying the lookup method.
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 lookup operation with no output schema and no annotations), the description is incomplete. It doesn't explain what the tool returns (e.g., user object, boolean), error scenarios, or how it integrates with sibling tools in the Slack context. For a tool that likely interacts with user data, more context is needed to understand its full scope and limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal meaning beyond the input schema, which has 100% coverage and clearly documents the single parameter 'email'. The description implies the parameter is used for lookup but doesn't provide additional context like email format requirements or case sensitivity. With high schema coverage, the baseline score of 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 tool's purpose with a specific verb ('lookup') and resource ('user'), and specifies the lookup method ('by their email address'). It distinguishes itself from generic user lookup tools by focusing on email-based identification. However, it doesn't explicitly differentiate from sibling tools like 'slack_get_user_profile' or 'slack_get_users', which might also retrieve user information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., 'slack_get_users' for listing users or 'slack_get_user_profile' for detailed profiles) or specify contexts like needing email-based identification versus other identifiers. Usage is implied by the name 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.
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. While 'Add' implies a mutation, it doesn't specify permissions required, rate limits, whether reactions are reversible, or what happens on success/failure. For a mutation 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 with zero waste—it directly states the tool's purpose without unnecessary elaboration. It's appropriately sized and front-loaded, making it easy to understand at a glance.
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 required parameters) and lack of annotations or output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, permissions, or response format, which are crucial for an AI agent to use it correctly in context with sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain emoji format conventions beyond 'without ::' or provide 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 ('Add a reaction emoji') and the target ('to a message'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'slack_post_message' or 'slack_reply_to_thread', which are also message-related actions but serve different purposes.
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 message access), exclusions, or comparisons to sibling tools like 'slack_post_message' for creating messages or 'slack_reply_to_thread' for threaded replies.
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. It lacks details such as whether this is a read-only operation, how messages are ordered, if there are rate limits, or what the output format looks like, which are critical for a tool with no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, making it easy to parse. It's appropriately sized for the tool's complexity and front-loaded with the core action, earning full marks for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like message ordering, pagination, or error handling, nor does it explain return values. For a tool with two parameters and missing structured context, this leaves significant gaps.
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 ('channel_id' and 'limit') adequately. The description adds no additional meaning beyond implying retrieval of messages, which aligns with the schema but doesn't enhance parameter understanding beyond the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('recent messages from a channel'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'slack_get_thread_replies' or 'slack_list_channels' beyond the basic scope, 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. For example, it doesn't mention when to choose this over 'slack_get_thread_replies' for thread-specific messages or 'slack_list_channels' for channel metadata, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read-only operation, it doesn't specify permissions needed, rate limits, pagination behavior, or what the return format looks like. For a tool with zero annotation coverage, this leaves critical behavioral traits unaddressed.
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's appropriately sized and front-loaded, making it easy to understand at a glance with zero waste.
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 the tool returns (e.g., reply structure, metadata), error conditions, or behavioral constraints. For a tool that fetches data with no structured output documentation, this leaves too many contextual gaps.
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 both parameters clearly documented in the input schema. The description adds no additional meaning beyond what the schema provides, such as explaining the relationship between 'channel_id' and 'thread_ts' or providing examples. 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 verb 'Get' and the resource 'all replies in a message thread', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'slack_reply_to_thread' or 'slack_get_channel_history', 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 when to choose this over 'slack_get_channel_history' for thread replies or how it relates to 'slack_reply_to_thread'. Without any usage context or exclusions, this is a significant gap.
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 it 'Get[s] detailed profile information', which implies a read-only operation, but doesn't specify authentication needs, rate limits, error conditions, or what 'detailed' entails (e.g., fields returned). This is a significant gap for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it directly states the tool's purpose without redundancy. It's appropriately sized for a simple retrieval tool and front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed profile information' includes (e.g., fields like email, status, timezone), how errors are handled, or any behavioral traits. For a tool with rich potential output, this leaves critical gaps for an 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?
Schema description coverage is 100%, with the single parameter 'user_id' documented in the schema as 'The ID of the user'. The description adds no additional meaning beyond this, as it doesn't explain format (e.g., Slack user ID syntax) or provide 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 verb 'Get' and the resource 'detailed profile information for a specific user', making the purpose unambiguous. It doesn't explicitly distinguish from siblings like 'slack_get_users' (which likely lists multiple users), but the specificity of 'for a specific user' implies a single-user focus.
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 'lookup_user_by_email' or 'slack_get_users'. It mentions 'for a specific user' but doesn't clarify prerequisites (e.g., needing a user ID) or exclusions, leaving usage context implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Post a new message') but lacks details on permissions required, rate limits, error handling, or what happens on success (e.g., message ID returned). This is inadequate for a mutation tool with zero annotation coverage, as critical behavioral traits are missing.
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 appropriately sized, with every part contributing essential information, 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 complexity as a mutation operation with no annotations and no output schema, the description is incomplete. It fails to explain behavioral aspects like permissions or response format, and while the schema covers parameters, the overall context for safe and effective use is lacking, making it insufficient 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 input schema has 100% description coverage, with clear parameter descriptions for 'channel_id' and 'text'. The description does not add any semantic details beyond what the schema provides (e.g., format examples or constraints), so it meets the baseline score of 3 where the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Post a new message') and target resource ('to a Slack channel'), making the purpose immediately understandable. However, it does not explicitly differentiate this tool from sibling tools like 'slack_reply_to_thread' or 'slack_add_reaction', which also involve posting content in Slack, leaving some ambiguity in sibling 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. It does not mention prerequisites (e.g., channel access), exclusions (e.g., not for threads or reactions), or refer to sibling tools like 'slack_reply_to_thread' for thread-specific replies, 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?
With no annotations provided, the description carries full burden but only states the basic action without behavioral details. It doesn't disclose whether this requires specific permissions, rate limits, how errors are handled, or what happens if the thread doesn't exist. For a mutation 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, focused sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential action 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?
For a mutation tool with no annotations and no output schema, the description is insufficient. It lacks critical context about permissions, error conditions, response format, and differentiation from sibling tools, making it incomplete for safe and effective use by 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?
Schema description coverage is 100%, so the schema fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's already in the schema, maintaining the baseline score 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 ('Reply to') and target ('a specific message thread in Slack'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'slack_post_message' or 'slack_add_reaction', 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 'slack_post_message' (for new messages) or 'slack_add_reaction' (for reactions). It also doesn't mention prerequisites such as needing channel access or thread existence, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'Get[s] a list' but doesn't mention whether this requires specific permissions, rate limits, pagination behavior beyond the cursor parameter, or what 'basic profile information' includes. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational traits.
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, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (list retrieval with pagination), no annotations, and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on permissions, rate limits, output format, or how to interpret 'basic profile information', leaving gaps that could hinder effective agent 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?
The schema description coverage is 100%, with clear descriptions for both parameters (cursor for pagination, limit with default and max). The description adds no additional parameter semantics beyond what the schema provides, such as explaining how pagination works or typical use cases for limit adjustments. 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 'Get' and resource 'list of all users in the workspace with their basic profile information', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'slack_get_user_profile' (which might get detailed info for a single user) or 'lookup_user_by_email' (which finds specific users), missing full sibling 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. It doesn't mention when to choose this over 'lookup_user_by_email' for finding specific users, 'slack_get_user_profile' for detailed single-user info, or other sibling tools, leaving the agent without explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'pagination' and implies a read-only operation by using 'List', but doesn't specify authentication requirements, rate limits, or error conditions. It adds some value by clarifying the scope ('public channels') but lacks depth for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('List public channels in the workspace') and includes a key behavioral trait ('with pagination') without any wasted words. Every element serves a clear purpose, 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.
Completeness3/5
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 minimally adequate for a read-only list tool. It covers the resource scope and pagination but omits details like return format, error handling, and authentication needs. For a tool with 2 parameters and 100% schema coverage, it meets basic requirements but lacks completeness for robust agent 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 fully documents both parameters ('limit' and 'cursor') with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, resulting in a baseline score of 3 as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('public channels in the workspace'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'slack_get_channel_history' or 'slack_get_users', which also retrieve Slack data but serve different purposes.
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., authentication), exclusions (e.g., private channels), or compare it to siblings like 'slack_get_users' for user-related queries, leaving the agent to infer usage context.
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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