kafka-sentinel-mcp

Server Quality Checklist

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Latest release: v0.1.5
Disambiguation4/5
Most tools target clearly distinct concerns: health, lag, audit, partition state, replay readiness. However, cluster_health and incident_snapshot overlap in scope since the snapshot bundles the health data, and topic_audit vs partition_state both touch replication/durability concerns at the partition level, creating minor confusion.
Naming Consistency4/5
Tool names use a consistent noun-based pattern (adjective_noun) like cluster_health, consumer_lag, topic_audit, partition_state — not strictly verb_noun but internally consistent. Minor deviation is having two 'list_' verbs mixed in with the descriptive noun pattern, though this reads as intentional since they are discovery operations.
Tool Count4/5
Eight tools for a Kafka operational monitoring server is a reasonable, focused scope. Each tool maps to a distinct operational concern with no obvious redundancy, and the incident_snapshot aggregation is a valuable convenience rather than bloat.
Completeness4/5
The surface covers the core read-only operational concerns an agent would need: discovery (list_topics, list_consumer_groups), health, lag, audit, partition state, and replay/retention analysis. Minor gaps exist — there's no consumer group reset or topic config mutation (read-only by design), and no offset trimming tool, but for an operational sentinel these are reasonable absences.
Average 3.5/5 across 8 of 8 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 commits in the last 12 weeks
- Last stable release on July 31, 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided and an output schema present, the description carries the full disclosure burden. It explains the question being asked but doesn't disclose behavioral details: whether it's read-only, whether it queries brokers/admin servers, whether it considers compaction vs. deletion retention, or what edge cases exist (e.g., compacted topics). The output schema exists but the description doesn't define what values/tags the tool returns to answer the readiness question.
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 question. It's front-loaded and wastes no words, but it's under-specified rather than optimally concise. A well-crafted description could add behavioral and output detail in 2-3 sentences without losing the brevity.
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 there's an output schema but no annotations, the description should at least clarify the tool's behavior and return semantics. It doesn't explain what the readiness answer looks like, what thresholds matter, or how retention deletion is determined. For a tool with a nuanced Kafka concept (replayability vs retention vs offsets), the description is too thin to fully guide an agent.
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 the description must compensate for what group and topic mean in this context. The description implies group is a consumer group and topic is the Kafka topic, but doesn't add meaning beyond their names/titles. It doesn't clarify required format, whether the topic is the source topic being replayed from, or any relationship between the two parameters beyond both being inputs.
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 tells you the tool checks whether a group can replay everything since committed offsets or if retention has deleted part of the gap. It names the resource (group+topic) and the question being answered. However, it doesn't state what the tool outputs (a boolean? a percentage? a message?) and doesn't use a verb like 'check' or 'evaluate' explicitly; it's phrased as a question rather than an action. The description is reasonably distinct from siblings like consumer_lag and topic_audit, though it doesn't explicitly differentiate.
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?
The description implies usage for assessing replay capability, which involves retention and committed offsets context. It gives a clear scenario where the tool applies. However, it doesn't contrast with alternatives or state when NOT to use it, and doesn't mention whether this should be used alongside consumer_lag or topic_audit for a fuller picture.
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 behavioral disclosure burden. It doesn't disclose whether this is a read-only operation, whether it returns raw metrics or computed diagnostics, or what 'leader skew' implies for interpretation. The read-only nature is implied but never stated, and no context is given about what the returned data means or how it should be used.
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 sentence that conveys the tool's function efficiently. Zero wasted words; the description is appropriately sized for a straightforward read tool.
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?
With no annotations, no param descriptions, and no usage guidance for a diagnostic tool among seven siblings that could all plausibly be invoked for similar questions, the description is too thin. The output schema exists but the description doesn't explain what consumers should do with the returned leader skew or ISR state, leaving the tool's interpretive value unexplained.
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?
Despite 0% schema description coverage, the description contextually connects the single 'topic' parameter to the leader/ISR state reporting. The single parameter is self-explanatory given the tool purpose, so the schema plus description is adequate. However, the description adds no format, range, or example values beyond what the schema title 'Topic' provides.
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 reports 'Leader/ISR state per partition' with leader skew across brokers, which is a specific resource and provides the core purpose. However, it doesn't offer terminology that would help an agent distinguish it from siblings like cluster_health or topic_audit, and it leaves the metric interpretations vague.
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 when-to-use guidance is provided. The description doesn't state when to choose this over cluster_health (health at cluster level), topic_audit, or consumer_lag. No exclusions or preferred contexts are mentioned, leaving the agent to infer based on 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. The description is safely a read/summary operation by nature, but doesn't state whether it requires special permissions, whether it reflects live or cached data, or what the output format looks like despite an output schema existing. Minimal behavioral disclosure beyond the operation itself.
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?
A single efficient sentence that enumerates the key health dimensions. No wasted words, though it functions more as a label than a rich description.
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?
There's an output schema present which reduces the need to explain return values. The description covers the core health dimensions but falls short on usage context—it doesn't clarify the relationship to sibling tools like incident_snapshot or partition_state, making the overall completeness only adequate for a zero-param tool.
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?
With 0 parameters and 100% schema coverage, there is nothing for the description to explain about parameters. The baseline for 0-param tools is 4, and the description appropriately lists what is summarized, adding value about the tool's scope.
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 names the resource (broker count, controller, under-replicated/offline partitions) with a clear summary verb implied. It's identifiable as a health overview tool, but lacks a specific verb (get, list, summarize) and doesn't clearly distinguish from siblings like partition_state, which could overlap on partition health metrics.
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 the sibling tools. Siblings like partition_state, incident_snapshot, and consumer_lag all touch health/status areas, yet there's no differentiation provided. An agent would struggle to know when cluster_health is preferred over incident_snapshot or partition_state.
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. It does add useful behavioral context: it defines what 'committed' means and specifically notes that committed = -1 means no offset stored, which is a meaningful edge-case disclosure. However, it doesn't describe read-only safety, pagination, ordering, or what happens if group/topic doesn't exist. For a clearly read-only diagnostic tool, this is adequate but not rich.
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 tight single sentence stating the metric, scope, and a key edge-case definition. The 'committed = -1' clarification is valuable exactness. Every clause earns its place; no waste. Could be slightly better structured as two sentences for readability but is appropriately concise.
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?
There is an output schema present, so return-value details are handled externally. With no annotations, the description covers the core meaning and the critical -1 edge case. However, for a lag diagnostic tool, it could clarify whether lag is computed per-group+per-topic across all partitions (it does say per-partition), and could note whether it's a live computation or sampled. The absence of annotation safety info is partly mitigated by the clearly diagnostic nature of the tool.
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 both parameters (group, topic) are completely undocumented in the schema. The description does identify both parameters in its prose ('consumer group on a topic'), which maps group and topic to their roles. However, it doesn't add format, constraint, or behavior details beyond identifying which variables exist. It partially compensates for the 0% coverage gap.
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 it's a per-partition lag tool for a consumer group on a topic, defining committed offset vs log-end offset. It distinguishes itself reasonably from siblings like cluster_health and topic_audit by focusing specifically on consumer lag, though it doesn't explicitly name a sibling alternative. The verb 'lag' with the resource specifics (consumer group + topic) makes the purpose specific.
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?
The description implies this is used to inspect lag for a consumer group on a topic (lag matters for replay/consumption delays), and among siblings like replay_readiness this is the direct lag tool. However, there's no explicit when-to-use/when-not-to-use guidance or mention of alternatives. The context of sibling tools suggests distinct purposes, but the description doesn't draw those boundaries.
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. The description indicates it returns timestamped data and is a bundle, but lacks details on authentication needs, error handling, or whether individual component failures affect the snapshot.
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 very concise (one sentence and a short phrase), front-loads the key concept ('One-call bundle'), and contains no redundant 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?
Given the tool's complexity (bundle of 5 components) and lack of output schema, the description provides only high-level context. It does not specify output format, error behavior, or how the snapshot is assembled, leaving some gaps for the agent.
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 input schema has 0% description coverage, and the tool description does not explain the parameters 'group' and 'topic' beyond their names, leaving ambiguity about their exact meaning (e.g., consumer group vs. topic).
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 it is a one-call bundle combining cluster health, lag, audit, partition state, and replay readiness, explicitly distinguishing it from the sibling tools that cover each aspect individually.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for postmortems and comprehensive snapshots, and contrasts with sibling tools that provide individual data, but does not explicitly state when not to use this tool or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the tool is read-only/audit in nature ('audit', 'flags settings'), and indicates it compares configs against 'mission-critical best practice', which hints at judgment-based evaluation. The output schema exists to convey results format, reducing the transparency burden further.
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?
Two sentences, zero wasted words. Each sentence adds distinct value: the first identifies the resource and focus, the second explains the tool's evaluation behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only audit tool with an output schema, the description is reasonably complete. It explains what is audited, the evaluation standard, and flags behavior. It could mention what 'best practice' entails (e.g., replication factor thresholds), but given the output schema conveys results, this is largely sufficient.
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 there is only a single parameter ('topic'), which is self-explanatory given the name and description. The description adds 'replication and durability config' context about what the audit examines, but the single string parameter requires no further elaboration. Baseline 3 is appropriate given the near-zero need for parameter explanation.
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?
'Replication and durability config audit for a topic' clearly states a specific verb ('audit') and resource ('topic'), focused on replication and durability configuration. It distinguishes reasonably from siblings like cluster_health (cluster-wide) and partition_state (specific partition view), though it could name an alternative explicitly.
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?
The description implies the tool should be used when checking a topic's replication/durability settings against best practice, and 'flags settings that violate mission-critical best practice' clarifies its audit nature. However, it doesn't explicitly say when NOT to use it or name siblings as alternatives (e.g., when to use partition_state instead for per-partition detail).
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 states the output includes group IDs and state but doesn't disclose pagination behavior, potential performance cost on large clusters, or whether group state is real-time vs cached. For a read/list operation this is acceptable but could add more context.
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?
Two sentences, zero wasted words. Purpose and usage guidance are cleanly separated and front-loaded. It earns every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values don't need explanation. For a parameterless list tool with clear purpose and explicit usage guidance, the description is largely complete. It could add a note about volume/cost on large clusters, but the core information is present.
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 takes zero parameters and schema coverage is 100% (trivially complete). A parameterless tool needs no parameter documentation, so the baseline 4 applies. No additional param semantics are needed or missing.
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+scope: 'List all consumer group IDs on the cluster with their state.' It specifies the resource (consumer groups), the scope (all, on the cluster), and the output detail (state). It distinguishes itself from siblings like consumer_lag and list_topics by focusing on group IDs+state, though it doesn't explicitly name alternatives.
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 usage guidance: 'Use this first if you don't already know a group name.' This directly tells the agent when to invoke it versus its sibling consumer_lag, which presumably takes a specific group name. This is model-worthy guidance placed right after the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 'non-internal' filtering behavior and the fields returned (partition count, replication factor). This adds meaningfully beyond the empty schema, though it doesn't discuss potential latency or side-effect details beyond the read-only nature implied.
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?
Two sentences with zero waste. The first sentence states the core function, the second provides placement guidance. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only list tool with an output schema, the description covers the filtering scope, return fields, and usage context. It's nearly complete; the only minor gap is not explicitly noting it's a read-only operation, though that's strongly implied.
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's no parameter documentation burden. The description adds value by describing the return content (partition count, replication factor), which compensates for the empty schema.
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?
Clearly states it lists non-internal topics with partition count and replication factor. The verb+resource+scope is specific and distinguishes it from siblings like consumer_lag and partition_state which focus on different aspects of topic state.
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?
Explicitly advises to use this tool first if you don't already know a topic name, noting that every other tool needs one. This is clear, actionable guidance on when to invoke this tool versus alternatives.
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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