slack-qa-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
The tools have mostly distinct purposes, though 'scan_qa_bugs' and 'list_items' both involve retrieving QA-related information, which could cause mild confusion. However, their descriptions clarify that one scans a Slack channel for bug reports while the other reads from a specific QA Slack list, helping to differentiate them.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern (e.g., get_bug_detail, list_items, post_fix_update) with clear and descriptive terms. There are minor deviations, such as 'scan_qa_bugs' using 'scan' instead of a more standard verb like 'list' or 'get', but overall the pattern is predictable and readable.
Tool Count5/5
With 5 tools, the count is well-scoped for a QA-focused Slack server. Each tool appears to serve a specific and necessary function in the QA workflow, from scanning and listing bugs to posting updates and managing item statuses, without feeling overly sparse or bloated.
Completeness4/5
The toolset covers core QA operations like retrieving bug details, scanning and listing items, posting updates, and updating statuses, which supports a basic workflow. A minor gap exists in not having a tool to create or delete QA items, but agents can likely work around this with the provided tools for most common tasks.
Average 2.7/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 2 community issues answered or closed in the last 6 months
- 3 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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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. While 'Read items' implies a safe, non-destructive operation, the description doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what the output format looks like. For a read tool with zero annotation coverage, this leaves significant behavioral gaps.
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 gets straight to the point. It wastes no words and is appropriately sized for a simple read tool. However, it could be slightly more structured by explicitly mentioning it's for reading items from a specific list.
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 'items' are returned, their format, or any behavioral traits like error handling. For a tool with 2 parameters and siblings that might overlap in functionality, more context is needed to ensure proper 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%, with both parameters ('list_id' and 'status_filter') well-documented in the schema. The description adds no additional parameter information beyond what's already in the schema (e.g., it doesn't clarify the Korean text '피드백 추적기' or provide examples beyond the schema). 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Read items from the QA Slack List - 피드백 추적기', which clearly indicates it's a read operation on a specific resource (QA Slack List). However, it doesn't distinguish this from sibling tools like 'scan_qa_bugs' or 'get_bug_detail', which might also retrieve QA-related data. The purpose is clear but lacks 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 this tool is appropriate compared to siblings like 'scan_qa_bugs' or 'get_bug_detail', nor does it specify any prerequisites or exclusions. The user must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 mentions scanning for 'recent QA bug reports' but doesn't specify what 'recent' means (though the parameter covers this), the output format (e.g., list, summary, raw messages), or any side effects like rate limits or authentication needs. For a tool with no annotations, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the core action, but the bilingual repetition ('버그 리포트 스캔') is redundant and doesn't add clarity. It's appropriately sized for a simple tool, with no wasted sentences, though the redundancy slightly detracts from optimal 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, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what the tool returns (e.g., bug summaries, messages, IDs), how results are structured, or any error conditions. For a scanning tool that likely outputs data, this lack of context makes it inadequate for an agent to use effectively without guesswork.
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 'days_back' fully documented in the schema (type, range, default, description in Korean). The description adds no additional meaning beyond the schema—it doesn't explain how 'days_back' relates to 'recent' or provide usage examples. Baseline is 3 since the schema does the heavy lifting, but the description doesn't compensate or add value.
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 scans a specific channel (#axr-qa) for QA bug reports, which provides a clear verb (scan) and resource (channel). However, it doesn't differentiate from sibling tools like 'get_bug_detail' or 'list_items'—it's vague about whether this returns summaries, full reports, or just counts. The bilingual phrasing adds minor confusion but doesn't fundamentally obscure 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., channel access), exclusions, or comparisons to siblings like 'get_bug_detail' (for single bugs) or 'list_items' (possibly general listing). The description implies a scanning action but offers no context for selection among similar tools.
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, the description carries full burden but only states it's an update operation without disclosing behavioral traits like permissions needed, whether changes are reversible, rate limits, or what happens on success/failure. It mentions 'QA item' but doesn't clarify the system or effects.
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 with no wasted words, but the bilingual parenthetical is redundant and doesn't add value. It's front-loaded with the core action, though it could be more structured with usage context.
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 incomplete. It lacks details on what 'QA item' refers to, how status changes affect the system, error conditions, or return values, leaving significant gaps for 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?
Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no meaning beyond the schema—it doesn't explain parameter relationships, provide examples beyond the schema's 'e.g.', or clarify semantics like what '상태' means in context.
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 'Update a QA item's status (상태 업데이트)' clearly states the verb ('Update') and resource ('QA item's status'), but it's vague about what a 'QA item' is and doesn't distinguish this tool from its siblings like 'post_fix_update' or 'scan_qa_bugs'. The bilingual parenthetical adds no 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?
No guidance is provided on when to use this tool versus alternatives like 'post_fix_update' or 'scan_qa_bugs'. The description implies it's for status updates but doesn't specify context, prerequisites, or exclusions, leaving the agent with no usage 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 the full burden of behavioral disclosure. It states this is a read operation ('get'), implying it's non-destructive, but doesn't cover other aspects like authentication requirements, rate limits, error handling, or what 'detailed info' includes (e.g., fields, format). 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence and no wasted words. It's front-loaded with the core purpose, though the Korean translation adds minor redundancy. It could be slightly improved by integrating the translation more seamlessly, but overall it's efficient.
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 'detailed info' returns (e.g., bug fields, status, history), which is critical for a tool focused on retrieval. For a read operation with no structured output documentation, this leaves the agent guessing about the response format.
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 'thread_ts' documented as '버그 메시지의 thread_ts' (bug message's thread_ts). The description adds no additional parameter semantics beyond this, such as format examples 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 as 'Get detailed info on a specific bug thread' with a Korean translation. It specifies the verb 'get' and resource 'bug thread', making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'scan_qa_bugs' or 'list_items', which might also retrieve bug information in different ways.
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 needing a thread_ts from another source, or compare it to siblings like 'scan_qa_bugs' for broader searches or 'list_items' for summaries. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 discloses the target channel (#axr-qa) and that it's for sharing fix updates, but lacks critical behavioral details: it doesn't specify if this is a read-only or mutative operation (though 'Post' implies creation), authentication needs, rate limits, error handling, or what happens on success (e.g., message posted). For a tool with no annotation coverage, this is a significant gap.
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 action and target. The Korean translation ('수정 내용 공유') adds clarity for Korean-speaking users without redundancy. There's no wasted text, making it appropriately concise for the tool's 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?
Given no annotations and no output schema, the description is incomplete for a tool with 3 parameters and implied mutation. It lacks details on behavioral traits (e.g., permissions, side effects), usage context, and return values. While the schema covers parameters well, the description doesn't compensate for missing annotation and output information, making it inadequate for full 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 already documents all three parameters (thread_ts, message, pr_url) with descriptions in Korean. The description adds no parameter-specific semantics beyond implying that 'message' contains the fix update content. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, with no extra value from 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 action ('Post a fix update') and target ('to #axr-qa channel'), with the Korean translation adding context. It distinguishes from siblings like 'get_bug_detail' or 'scan_qa_bugs' by focusing on posting updates rather than retrieving or scanning data. However, it doesn't specify what constitutes a 'fix update' versus other updates, leaving some ambiguity.
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., when a fix is ready), exclusions, or how it relates to sibling tools like 'update_item_status' for status changes. Usage is implied only by the action of posting to a specific channel, with no explicit context for selection.
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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