Slack Enterprise MCP Server

Server Quality Checklist

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Latest release: v1.0.7
Disambiguation4/5
Each tool targets a distinct Slack operation (channel creation, audit log, threads, channels listing, search, messaging, topic setting, summarization). However, the generic boilerplate descriptions about 'analysis and classification' are confusing and could mislead an agent about the actual purpose.
Naming Consistency5/5
All tool names follow a clear verb_noun pattern in snake_case (e.g., create_channel, get_audit_log, search_messages). No mixing of camelCase or other conventions.
Tool Count4/5
8 tools is a reasonable number for a Slack server. It covers essential operations without being too few or too many, though the domain could support a few more.
Completeness3/5
The toolset covers basic channel and messaging operations but lacks channel deletion, archiving, user management, and message editing/deletion. The audit log is a plus for enterprise, but notable gaps exist.
Average 2.3/5 across 8 of 8 tools scored. Lowest: 1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 25 commits in the last 12 weeks
- Last stable release on June 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The behavioral transparency section provides extensive details about read-only, idempotent, and stateless behavior, but these directly contradict the tool's implied mutation purpose (updating a channel topic). Since no annotations are provided, the description is the sole source of behavioral info, and it is incorrect and misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long and contains much irrelevant information (analysis, classification, rate limits for pro tier) that does not pertain to updating a channel topic. Important details are buried, and the structure is not front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's 3 parameters (2 required) and no output schema, the description fails to explain what the tool returns, error handling specifics, or how to correctly use the parameters. It is completely inadequate for an agent to correctly invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage and the description's 'Args' section merely repeats parameter names (channel, topic, api_key) without adding any semantic meaning. For a tool that updates a Slack channel topic, there is no explanation of what values are valid for channel (ID vs name) or how topic should be formatted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts by stating 'Update the topic of a Slack channel', which matches the tool name, but then immediately contradicts this by describing the tool as 'read-only and stateless' and focused on 'analysis or classification'. This is misleading and fails to clearly state the tool's actual purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided for when to use this tool for updating a channel topic. The description discusses general analysis use cases, which are irrelevant to the tool's core function, and does not distinguish it from sibling tools like create_channel or send_message.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Declares itself read-only and stateless, which directly contradicts the tool's name and introductory purpose of sending messages. Misleading and unreliable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Excessively long with redundant sections (Behavior and Behavioral Transparency overlap). Despite length, it fails to be clear or concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite extensive text, the description omits fundamental details such as whether the tool actually sends messages or performs analysis. Lacks coherence with siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. However, parameter descriptions are generic and repetitive ('the channel to analyze or process'), adding little meaning beyond parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description first states it sends a message, then contradicts by claiming read-only analysis. This inconsistency severely undermines purpose clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides conflicting guidance: one section says use for sending messages, another says for structured analysis. No clear instructions on when to use vs siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims the tool is read-only, has no side effects, and is idempotent, which directly contradicts the tool's purpose of creating a channel. This is misleading and harmful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long with repeated sections (e.g., 'Behavioral Transparency' appears twice) and includes irrelevant boilerplate, making it verbose and poorly structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a create operation with no output schema, the description fails to explain the actual behavior, side effects, return values, or authentication requirements for creating a Slack channel, leaving critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter descriptions in 'Args' are generic placeholders ('The name to analyze or process') that add no domain-specific meaning for channel creation, failing to compensate for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Create a new Slack channel' but immediately contradicts this by stating the tool 'generates structured output without modifying external systems' and is 'read-only', severely muddling the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'When to use' and 'When NOT to use' sections discuss 'structured analysis or classification', which is irrelevant to channel creation, providing no useful guidance for this tool.
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 must cover behavioral traits. It includes a detailed section claiming no side effects, no authentication for basic usage, and local processing, but these contradict the presence of an 'api_key' parameter and the likely external API call. The description is internally inconsistent, reducing trustworthiness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly long with repetitive and irrelevant sections (e.g., generic transparency details not specific to get_thread). The first sentence is concise, but subsequent content is verbose and dilutes clarity.
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?
The tool has 4 parameters, no output schema, and no enums, yet the description omits return value structure, pagination info, or error cases specific to thread fetching. The generic error handling and behavior sections do not compensate for missing specifics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must add meaning. The 'Args' section merely restates parameter names with generic phrases like 'The channel to analyze or process', adding no new information. It fails to explain format (e.g., thread_ts timestamp syntax) or the role of api_key.
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 first line clearly states the tool retrieves a full thread with replies, but the subsequent text describes it as providing structured analysis or classification, which is incongruent and confuses the purpose. The tool is partly distinguishable from siblings (get_thread vs. send_message), but the conflicting descriptions undermine clarity.
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 'When to use' and 'When NOT to use' sections are generic, referencing 'structured analysis' and 'real-time production decision-making', which do not align with the thread retrieval purpose. No guidance is given on when to prefer this tool over siblings like search_messages or list_channels.
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 includes a detailed 'Behavioral Transparency' section claiming read-only, stateless, and idempotent behavior, but this appears copy-pasted from a generic analysis tool and contradicts the Slack search context (e.g., 'No authentication required' is likely false for Slack). The inconsistency reduces trust.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long and repetitive, with a main description, a 'When to use' section, an 'Args' list, and a redundant 'Behavioral Transparency' block. The structure is disjointed, and the inclusion of unrelated generic content wastes tokens.
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?
The description lacks an explanation of return values or output format, and the generic analysis content dilutes the Slack messaging context. It does not clarify when to use this tool over siblings like get_thread or list_channels, leaving the agent without sufficient context to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description's 'Args' section merely repeats parameter names with generic phrases like 'The query to analyze or process.' It fails to explain how to use Slack-specific modifiers or what valid values are for 'sort' and 'count', leaving agents without meaningful guidance.
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 opens with a clear purpose: 'Search messages across the entire Slack workspace.' However, later sections (e.g., 'When to use') describe a generic analysis or classification tool, creating confusion and undermining the initial clarity.
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 'When to use' and 'When NOT to use' sections are overly generic and do not help differentiate from sibling tools like list_channels or get_thread. They reference 'structured analysis' rather than message searching, providing poor guidance for tool selection.
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?
The 'Behavioral Transparency' section includes details on side effects, authentication, rate limits, error handling, and idempotency, which is informative. However, the content is largely generic (e.g., mentions 'MEOK API key') and may not accurately reflect Slack channel behavior, reducing relevance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy, redundant (e.g., 'Behavior' and 'Behavioral Transparency' overlap), and includes irrelevant generic boilerplate. It is not concise and lacks clear structure focused on the tool's specific purpose.
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?
The description omits essential details like what the tool returns (e.g., member counts, topics) and how to use it in Slack context. Despite good annotations coverage, the generic text fails to provide a complete and accurate picture for this specific tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The 'Args' section provides generic descriptions ('The limit to analyze or process') that add no meaningful context beyond the schema. Schema coverage is 0%, so the description fails to clarify what the parameters (limit, types, api_key) actually represent for listing channels.
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 first sentence clearly states the tool lists Slack channels with member counts and topics, providing a specific verb and resource. However, subsequent generic text (about 'analysis output', 'classification') dilutes clarity and does not differentiate from siblings like create_channel or get_audit_log.
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 'When to use' and 'When NOT to use' sections are generic and irrelevant to listing Slack channels. They do not guide the agent on when to choose this tool over siblings (e.g., search_messages, get_thread) or provide context-specific usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and excels: it explicitly states read-only nature, no side effects, rate limits (10/day free, unlimited pro), authentication details, error handling, and idempotency. This is comprehensive and exceeds requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy with redundancy between 'Behavior' and 'Behavioral Transparency' sections. The 'Args' section is unnecessary given schema. It is structured with headings, but could be more concise without losing key information.
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?
Covers many aspects (purpose, behavior, rate limits, error handling, privacy) but lacks details on parameters and output structure. No output schema exists, and the description does not describe what fields are returned. Parameter descriptions are insufficient. Overall, adequate but with gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the 'Args' section only repeats parameter names with generic phrases like 'The limit to analyze or process,' adding no meaningful semantics. The description does not explain what 'limit' means in context (e.g., number of records), nor what valid action_filter values are. Minimal value added over schema.
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 'Return the audit trail of all MCP actions performed through this server,' which is a specific verb+resource. It distinguishes from sibling tools like send_message or list_channels by focusing on compliance auditing. However, the 'When to use' section is generic and misaligned, slightly reducing clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Includes explicit 'When to use' and 'When NOT to use' sections, but the 'When to use' is vague and not specific to audit logs, saying 'structured analysis or classification of inputs'—which doesn't match the tool's purpose. No comparison with sibling tools is provided, limiting differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It thoroughly covers side effects (read-only, stateless, idempotent), authentication (none for basic, API key for pro), rate limits (10/day free, unlimited pro), error handling (structured errors), and data privacy (no storage or logging).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose and contains repetition (e.g., behavior details appear twice). While well-structured with sections, it could be more concise. Every sentence adds some value, but there is room for trimming.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no output schema), the description covers all necessary aspects: purpose, parameter semantics, behavioral traits (side effects, auth, rate limits, error handling, idempotency, privacy). It mentions return format ('messages grouped by topic threads, active participants, and key highlights') adequately.
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 0%, so description must compensate. The 'Args' section lists all three parameters but offers generic, tautological explanations ('The channel to analyze or process'). It does not explain defaults, constraints, or format beyond the schema. Minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the last N messages from a channel and produces a structured summary. It distinguishes itself from sibling tools like get_thread, search_messages, and list_channels by focusing on summarization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit sections for 'When to use' and 'When NOT to use' provide clear guidance: use for structured analysis, avoid for real-time production decisions without human review. This effectively differentiates usage scenarios.
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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