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AVIMBU

Slack MCP Server

by AVIMBU
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TypeScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves user information and the other posts messages, with no overlap in functionality. An agent can easily differentiate between them based on their descriptions.

    Naming Consistency5/5

    Both tools follow a consistent 'slack_verb_noun' pattern (slack_get_users, slack_post_message), using snake_case and clear action-object naming. This makes them predictable and easy to understand.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a Slack integration, lacking essential operations like reading messages, managing channels, or handling reactions. This minimal set limits agent capabilities significantly.

    Completeness2/5

    The tool surface is highly incomplete for a Slack server, missing core functionalities such as reading channel messages, listing channels, updating or deleting messages, and interacting with threads. This will cause frequent agent failures in typical Slack workflows.

  • Average 3/5 across 2 of 2 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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  • 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

  • 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 'Post a new message' implies a write operation, it doesn't mention authentication requirements, rate limits, error conditions, or what happens if the channel doesn't exist. This leaves significant behavioral gaps for a mutation tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple tool and gets straight to the point.

    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 doesn't explain what the tool returns, error conditions, authentication requirements, or any behavioral details beyond the basic action. Given the complexity of posting to Slack (which involves permissions, formatting, etc.), more context is needed.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage, with both parameters clearly documented in the schema. The description doesn't add any additional semantic context about the parameters beyond what's already in the schema, so it meets the baseline for high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Post a new message') and target resource ('to a Slack channel'), making the purpose immediately understandable. However, it doesn't differentiate from its sibling tool 'slack_get_users' beyond the obvious difference in function, which is why it doesn't reach 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 or any prerequisites for usage. It simply states what the tool does without context about appropriate scenarios or limitations.

    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 it indicates this is a read operation ('Get'), it doesn't mention important behavioral aspects like authentication requirements, rate limits, error conditions, or what 'basic information' specifically includes. For a tool that presumably accesses workspace data, this leaves significant gaps in understanding how it behaves.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a straightforward list operation and front-loads the essential information. Every word earns its place in this concise formulation.

    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?

    For a relatively simple list operation with 2 parameters and no output schema, the description provides adequate but minimal context. It covers what the tool does but lacks important operational details like authentication requirements, rate limits, and what specific user information is returned. Without annotations or output schema, the description should ideally provide more complete guidance about the tool's behavior and results.

    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 ('cursor' and 'limit') fully documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.

    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'), making the purpose immediately understandable. It specifies 'with basic information' which adds useful context about the scope of returned data. However, it doesn't explicitly differentiate from the sibling tool 'slack_post_message', which is a completely different operation.

    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, appropriate contexts, or comparison with the sibling 'slack_post_message' tool. The agent must infer usage purely from the tool name and description without any explicit guidance.

    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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