kafka-mcp

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool targets a distinct Kafka resource action: listing, describing, creating topics, plus producing and consuming messages. The two message tools (produce_message, consume_messages) are clearly opposite operations, so little confusion. Minor overlap between list_topics and describe_topic could cause slight ambiguity but descriptions are clear enough.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern: list_topics, describe_topic, create_topic, produce_message, consume_messages. The naming convention is uniform with no mixture of styles or vague verbs.
Tool Count4/5
Five tools is a reasonable, well-scoped count for a Kafka server covering core topic management and messaging. It's on the leaner side but every tool earns its place for the apparent purpose.
Completeness3/5
The surface covers topic lifecycle (list, describe, create) and basic messaging (produce, consume). Missing obvious operations like delete_topic, update_topic (partitions/replication), and consumer group management, which are common Kafka workflows an agent would expect.
Average 2.7/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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. It does not state whether consume_messages is a destructive operation (offset advancement, which can affect other consumers), whether it commits offsets back to Kafka, how it handles empty topics, timeouts, or whether consumers should use a unique group_id to avoid conflicting with other consumers. None of this is disclosed.
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?
At one sentence, it is concise and to the point, but it borders on under-specification rather than true conciseness. Every word earns its place, but the description is too thin to be helpful — it reads more like a summary than a specification.
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?
Despite having an output schema, the tool has real-world subtleties (Kafka consumer group semantics, offset management, blocking behavior) that are completely unaddressed. With 3 parameters at 0% schema-to-description coverage and zero annotations, this description leaves substantial behavioral and parameter ambiguities unresolved for a message-consuming 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?
Schema description coverage is 0%, yet the description adds no parameter-level information. The description doesn't clarify what group_id does (offsets are tracked per consumer group), doesn't explain the tradeoffs of max_messages, and doesn't mention that group_id defaults and multiple calls with the same group could interfere. The schema provides field types and defaults, but no semantic depth.
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 'Consume messages from a Kafka topic' which is a clear verb+resource pairing. However, it doesn't distinguish itself from sibling tools like produce_message (which is clearly different) or describe_topic — the purpose is minimally clear but offers no scoping detail beyond the generic action.
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., topic must exist, need create_topic first), no exclusions (e.g., not for reading topic metadata — use describe_topic), and no mention of blocking vs non-blocking consumption behavior.
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 of behavioral disclosure. It does not state whether creating an existing topic errors, whether configuration defaults to broker settings, whether partitions/replication are auto-created, or any idempotency characteristics. Minimal behavioral info for a mutating operation.
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?
Single sentence, efficient, no waste. But it is under-specified — brevity is appropriate yet could include non-redundant context like default behavior in the same compact structure.
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?
Despite having an output schema, the description is incomplete for a mutating operation with no annotations. It doesn't cover error cases (duplicate topic), broker requirements, or the significance of the replication_factor default. For a CRUD create operation, more behavioral context is expected.
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%, but the parameter names (topic, num_partitions, replication_factor) are self-explanatory with sensible defaults (1 partition, replication factor 3). The description adds nothing beyond schema, so it doesn't compensate for the 0% coverage, but the parameter semantics are inherently clear from naming and defaults.
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?
Description states 'Create a new Kafka topic' — verb+resource are clear. However, it provides no differentiation from siblings like describe_topic or list_topics. The purpose is functional but minimal, lacking any context about the create operation's role in the topic lifecycle.
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 on when to use this tool vs alternatives or prerequisites (e.g., broker availability, whether topic must not already exist). Siblings like produce_message/consume_messages imply different phases but no explicit guidance is given.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys that this is a write/mutation operation (producing a message), which is the core behavior. However, it doesn't disclose whether the topic is auto-created, whether the operation is synchronous or fire-and-forget, or what happens on failure — key gaps for a producer tool.
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 extremely short (a single sentence, 8 words), which makes it efficient but under-specified. It's front-loaded with the primary purpose. However, it has no structural elements (no sections) and uses almost no space for the multiple gaps identified in other dimensions.
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?
Despite having an output schema, the description omits critical context for a Kafka producer: serialization format expectations, whether this blocks until acknowledged (acks setting), behavior on non-existent topics, key/value null handling, or message size limits. The sibling tool consume_messages implies a read/write pairing, but the produce side is underdocumented. For a write tool interacting with infrastructure, this is a meaningful completeness gap.
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 description coverage is 0%, and the description adds no information about the semantics of the topic, key, or value parameters beyond what's in the schema (which itself is minimal — just types and defaults). Specifically, the description doesn't clarify the role of 'key' in partitioning/message ordering, or the expected format of 'value', or how null value/key are handled. With 3 parameters and 0% coverage, the description should compensate but doesn't.
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 'Produce a message to a Kafka topic' has a clear verb (produce) and resource (message to Kafka topic). It does distinguish from sibling tools like consume_messages, list_topics, and describe_topic, though it doesn't explicitly contrast them. It's adequate but the phrase 'produce a message' is somewhat generic — could add scope like producing to produce a single record vs batch.
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 vs alternatives, prerequisites (e.g., topic must exist, needing create_topic first), or Kafka producer-specific constraints. There's no mention of when-not-to-use or alternatives. The agent must infer usage context entirely from the sibling names.
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 behavioral disclosure burden. It doesn't state what information is returned (partitions, configs, offsets), whether the topic must already exist, or error behavior when describing a nonexistent topic. However, there is an output schema, which is not shown here, so some transparency may be expected from that.
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?
One short sentence with zero wasted words. Truly concise. Not as fully specified as it could be, but it would be hard to call this verbose.
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?
The tool is simple (1 param, 1 required) and has an output schema, which likely carries the return-value explanation. However, with no annotations and no guidance about prerequisites (topic must exist) or what 'describe' reveals beyond the output schema name, a bit more behavioral context would round it out. Adequate for a minimal tool but not enriched.
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?
There is only 1 parameter (topic) with 0% schema description coverage. The description doesn't add any meaning beyond the schema's 'topic' string field. However, the single-parameter tool is simple—'topic' is self-explanatory in context—so the baseline for a simple single-param tool is reasonable but not enhanced.
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 'Describe a Kafka topic.' uses a specific verb+resource, clearly indicating the operation (describe) and target (Kafka topic). It's understandable but minimal, and given sibling tools like list_topics and create_topic, it doesn't explicitly differentiate what 'describe' returns vs 'list' — the verb does most of the work.
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 siblings. With list_topics, create_topic, produce_message, and consume_messages present, there's no mention of when describe is appropriate (e.g., after creating a topic to verify configs, vs listing all topics to find it). Usage must be inferred entirely.
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 doesn't mention the return format, whether pagination applies, whether it reflects the live cluster state, required permissions, or latency/gating concerns. For a read-only enumeration tool with zero annotation coverage, more behavioral context is warranted.
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?
A single, efficient sentence with zero waste. Appropriate for a zero-parameter listing tool.
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 zero-parameter list tool with an output schema present, the description is reasonably complete for its core purpose. However, given the presence of siblings like create_topic, the description could add value by noting this is a read-only discovery operation, and could mention whether topic metadata includes configuration or offsets details.
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 zero parameters, so there is no parameter burden to carry. With schema description coverage at 100% and no params, the baseline of 4 applies. Nothing to add beyond what the schema conveys.
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?
Clear verb+resource: 'List all topics in the Kafka cluster.' This clearly states what it does. The word 'all' suggests it returns the full topic list, distinguishing it somewhat from describe_topic which targets a specific topic.
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 on when to use this vs alternatives. With siblings like describe_topic, create_topic, produce_message, consume_messages, the description could note that this is for enumeration/discovery while describe_topic handles individual topic details. No exclusions or context provided.
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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