What can you do with this server?

The Kafka MCP Enterprise Server provides governed, AI-agent-safe access to Apache Kafka through 11 tools and 6 resources, with enterprise-grade security controls. Core Capabilities: * Read cluster metadata: List or describe topics, consumer groups, and cluster details (tools: list_topics, describe_topic, describe_cluster, list_consumer_groups, describe_consumer_group). * Consume and produce messages: Read from topics with configurable limits and DLP scrubbing; write messages to topics with egress DLP scanning (tools: consume_messages, produce_message). * Manage topics: Create, alter configuration, or delete topics (tools: create_topic, alter_topic_config, delete_topic). Destructive operations (delete, ACL creation) require HMAC-signed approval tokens. * Manage ACLs: Create access control list entries (tool: create_acls). * Access resources: Retrieve topic lists, topic details, cluster info, consumer group lists, audit logs, and health status via kafka:// URIs. Security & Governance: * Fail-closed security pipeline: Enforces authentication, deny/allow lists, topic/group scoping, policy engine evaluation, taint guarding, approval gates, rate limiting, circuit breakers, validation, quarantine, identity propagation, sensitive topic gating, egress DLP, dry-run, consume clamps, byte bounds, post-execute DLP scrubbing, taint registration, and full audit with hash-chaining. * Data Loss Prevention (DLP): Detects and blocks/redacts sensitive data (e.g., emails, SSNs, credit cards, keys) on both consume and produce paths, with configurable categories. * Approval and taint tracking: Destructive actions require HMAC-signed approval tokens; tainted data is tracked to prevent information flow violations. * Resilience: Per-module circuit breakers (data_plane, control_plane), request rate limits, and rogue-agent quarantine. * Audit Trail: All decisions and actions are logged in a hash-chained ring buffer with identity and correlation IDs. * Observability: Monitor server liveness and circuit breaker states through health endpoints. The server implements JSON-RPC 2.0 for MCP methods (initialize, tools/list, tools/call, resources/list, resources/read).

Which integrations are available for this server?

Provides tools for managing Apache Kafka clusters, including listing and describing topics, consumer groups, cluster metadata, producing and consuming messages, creating and altering topics, and managing ACLs.

How do I use Kafka MCP Enterprise Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Kafka MCP Enterprise Server List all topics in the cluster" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Kafka MCP Enterprise Server

by vaquarkhan

Overview Schema Source Related Servers Discussions Admin

Python

Remote

The Kafka MCP Enterprise Server provides governed, AI-agent-safe access to Apache Kafka through 11 tools and 6 resources, with enterprise-grade security controls.

Core Capabilities:

Read cluster metadata: List or describe topics, consumer groups, and cluster details (tools: list_topics, describe_topic, describe_cluster, list_consumer_groups, describe_consumer_group).
Consume and produce messages: Read from topics with configurable limits and DLP scrubbing; write messages to topics with egress DLP scanning (tools: consume_messages, produce_message).
Manage topics: Create, alter configuration, or delete topics (tools: create_topic, alter_topic_config, delete_topic). Destructive operations (delete, ACL creation) require HMAC-signed approval tokens.
Manage ACLs: Create access control list entries (tool: create_acls).
Access resources: Retrieve topic lists, topic details, cluster info, consumer group lists, audit logs, and health status via kafka:// URIs.

Security & Governance:

Fail-closed security pipeline: Enforces authentication, deny/allow lists, topic/group scoping, policy engine evaluation, taint guarding, approval gates, rate limiting, circuit breakers, validation, quarantine, identity propagation, sensitive topic gating, egress DLP, dry-run, consume clamps, byte bounds, post-execute DLP scrubbing, taint registration, and full audit with hash-chaining.
Data Loss Prevention (DLP): Detects and blocks/redacts sensitive data (e.g., emails, SSNs, credit cards, keys) on both consume and produce paths, with configurable categories.
Approval and taint tracking: Destructive actions require HMAC-signed approval tokens; tainted data is tracked to prevent information flow violations.
Resilience: Per-module circuit breakers (data_plane, control_plane), request rate limits, and rogue-agent quarantine.
Audit Trail: All decisions and actions are logged in a hash-chained ring buffer with identity and correlation IDs.
Observability: Monitor server liveness and circuit breaker states through health endpoints.

The server implements JSON-RPC 2.0 for MCP methods (initialize, tools/list, tools/call, resources/list, resources/read).

Kafka MCP Enterprise Server

PyPI Python License

Scope: This is a stdio, in-memory conformance reference for the KIP-1318 security control model. Real brokers, Streamable HTTP, OAuth 2.1, EOS/fencing, Connect tooling, distributed rate/breaker state, and durable audit topics are specified in the KIP (Java production track under KAFKA-20436) and are not implemented here. Full matrix: doc/kip-alignment.md.

Implemented vs Spec-only

Area	This reference	KIP / Java track
Transport	stdio only	stdio + Streamable HTTP + OAuth
Backend	In-memory Kafka	Real Admin / Producer / Consumer
Tools	13 registered	~30+ (incl. Connect, EOS, group admin)
Resources	subset + Phase-1 lag/offsets	~20 `kafka://` URIs
Rate / quarantine	per-process	optional distributed backend
Audit	in-memory hash chain	durable audit topic
Default allow-list	read/consume only (`SECURE_DEFAULT_TOOLS`)	same secure-by-default posture
Conformance	302/302 checks	KIP test plan

Website


Site	https://vaquarkhan.github.io/kafka-mcp-enterprise-server/
Source	`site/` (`index.html` + local `site/assets/site.css`)
Deploy	`.github/workflows/pages.yml` publishes `site/` on push to `main`

PyPI

pip install kafka-mcp-enterprise
echo {"jsonrpc":"2.0","id":1,"method":"tools/list"} | kafka-mcp-enterprise


Package	`kafka-mcp-enterprise`
CLI	`kafka-mcp-enterprise`
Import	`import kafka_mcp`
Optional	`pip install kafka-mcp-enterprise[otel]`
Publish	Tag `v*` → `.github/workflows/publish.yml` (Trusted Publishing / OIDC - see doc/publishing.md)

Agents & skills (Cursor, Kiro, ChatGPT, Gemini, Copilot, …)


AGENTS.md	`AGENTS.md` - canonical instructions for every coding agent
Skills	`.cursor/skills/` (Cursor) · `skills/` (portable)
Guide	`doc/agents-and-skills.md` - how to load in each IDE

Python conformance reference for KIP-1318: a Model Context Protocol (MCP) control plane for agent access to Kafka. The KIP's production target is Java. This package validates the fail-closed security model over stdio against an in-memory backend - it is not a drop-in for the forthcoming first-party Java MCP server.


KIP	KIP-1318: MCP Server for Apache Kafka
Jira	KAFKA-20436 - Implement KIP-1318
Discuss	[DISCUSS] KIP-1318 on `dev@kafka.apache.org`
This repo	Stdlib Python reference / conformance server (teaching, demos, security validation)
KIP production target	Java module (`tools/mcp-server`) wrapping native Kafka clients - see the KIP

Scope clarity: The Apache Kafka project tracks the official implementation under KAFKA-20436. This repository is an independent, zero-dependency reference that encodes the enterprise control plane, error model, and conformance tests so designs can be validated before or alongside the Java work. It is not a drop-in replacement for the forthcoming first-party Java MCP server.

Why this exists

AI agents need governed Kafka access-not ad-hoc scripts, unbounded consumes, or shared "god" principals. KIP-1318 proposes a standalone MCP process (stdio / HTTP in the spec) that exposes tools and kafka:// resources without changing the Kafka wire protocol. This reference implements the fail-closed security control plane over stdio against an in-memory backend (see matrix above).

Broker ACLs remain authoritative. Guardrails here complement them; they never replace them.

What’s included

Kafka MCP Enterprise - what’s included

Documentation product image (not a web UI). Checklist of what this Python stdio reference implements: fail-closed pipeline, DLP, 13 tools, kafka:// resources, tests, and examples.

How it runs

flowchart LR
  Agent["Agent / IDE"]
  MCP["kafka-mcp-enterprise<br/>stdio JSON-RPC + pipeline"]
  Mem["InMemoryKafka<br/>conformance backend"]
  Agent -->|"MCP tools/call"| MCP
  MCP -->|"tools / resources"| Mem

This repo: Python stdlib reference (in-memory Kafka for tests). KIP production track: Java wrapping real Admin/Producer/Consumer (KAFKA-20436), not shipped here.

Fail-closed pipeline (overview)

flowchart TB
  A["1 Auth"] --> B["2 Deny-list"] --> C["3 Allow / readonly"]
  C --> D["4 Scope"] --> E["5 Policy"] --> F["6 Taint / IFC"]
  F --> G["7 Approval"] --> H["8 Rate limit"] --> I["9 Breaker execute"]

Full detail: doc/security-controls.md · architecture: doc/architecture.md.

Related MCP server: MCP Toolkit

Complete features, security controls & guardrails

Nothing below is optional marketing - every item is implemented in kafka_mcp/ and covered by the 302/302 conformance suite and/or demos/examples unless noted as a documented reference gap.

A. MCP protocol & surface

Feature	Detail
JSON-RPC 2.0	Strict `jsonrpc: "2.0"` request/response
`initialize`	`protocolVersion`, `serverInfo`, `capabilities`
`tools/list`	Visible tools honor deny-list, allow-list, and readonly
`tools/call`	Full fail-closed pipeline + handler
`resources/list`	Catalog of `kafka://` URIs
`resources/read`	Topic/cluster/group/audit/health reads
stdio transport	Newline-delimited JSON (`serve_stdio` / `kafka-mcp-enterprise`)
HTTP notes	Stateless HTTP design documented; full HTTP listener is a KIP/Java concern
Stateless approvals	HMAC tokens self-contained (no sticky session required for authz correctness)
Correlation IDs	Per-call `corr_id` on audit entries

B. Tools (13) - classified

Tool	kind	module	Kafka op
`list_topics`	read	control_plane	DESCRIBE
`describe_topic`	read	control_plane	DESCRIBE
`describe_cluster`	read	control_plane	DESCRIBE
`list_consumer_groups`	read	control_plane	DESCRIBE
`describe_consumer_group`	read	control_plane	DESCRIBE
`consume_messages`	read	data_plane	READ
`create_topic`	mutate	control_plane	CREATE
`alter_topic_config`	mutate	control_plane	ALTER
`produce_message`	mutate	data_plane	WRITE
`alter_consumer_group_offsets`	mutate	control_plane	ALTER
`delete_topic`	destructive	control_plane	DELETE
`delete_consumer_group`	destructive	control_plane	DELETE
`create_acls`	destructive	control_plane	ALTER

Shipped default exposes the six read tools only (SECURE_DEFAULT_TOOLS).

C. Resources (`kafka://`)

URI	Purpose
`kafka://topics`	List topics
`kafka://topics/{name}`	Describe topic
`kafka://topics/{name}/offsets`	Earliest/latest offsets per partition
`kafka://cluster`	Cluster id + brokers
`kafka://groups`	Consumer groups
`kafka://groups/{id}`	Describe a consumer group
`kafka://groups/{id}/offsets`	Committed offsets
`kafka://groups/{id}/lag`	Per-topic/partition lag (KIP Phase 1)
`kafka://audit/recent`	Recent audit entries
`kafka://health`	Liveness + per-module circuit breaker state

D. Fail-closed security pipeline (exact order)

Every tools/call - first denial wins:

Step	Control	Denial code
1	Bearer auth - audience / issuer validation (off until configured)	`-32001` UNAUTHORIZED
2	Deny-list (`tools_denied`)	`-32044` POLICY_DENIED
3	Allow-list (`tools_allowed`) + readonly (blocks all non-read, including produce)	`-32044`
4	Topic prefix scope + group prefix scope	`-32041` SCOPE_VIOLATION
5	Policy engine - callable; deny or exception → fail-closed	`-32044`
6	Taint guard / IFC - destructive tools; optional `ifc_strict`; approval bypasses	`-32040` TAINT_VIOLATION
7	Approval gate - HMAC signed TTL token (`_approval_token`)	`-32042` APPROVAL_REQUIRED
8	Rate limit - general vs admin/control-plane buckets	`-32029` RATE_LIMITED
9	Execute via per-module circuit breaker + dependency check	`-32043` DEPENDENCY_UNAVAILABLE

Pre / around execute (also enforced):

Guardrail	Behavior	Code
Input validation	Identifier charset; `max_value_bytes` on produce values	`-32046` VALIDATION_FAILED
Rogue-agent kill-switch	Per-identity destructive burst → quarantine	`-32047` QUARANTINED
Identity propagation	Optional per-principal broker ACL check before execute	`-32044`
Sensitive-topic gating	Pattern match on consume → requires approval	`-32042`
Egress DLP	Block secret categories on produce	`-32045` SENSITIVE_DATA_BLOCKED
Dry-run tools	`dryrun_tools` returns plan without mutation	-
Consume clamp	`maxMessages` capped by `hard_max_records`	-
Byte bounds	`hard_max_bytes` trims consume payload; `max_output_bytes` truncates scrubbed output	truncation tags
Post-execute DLP scrub	Redact/scrub whole result tree	-
Taint registration	Consumed values registered into session taint set	-
Audit	ALLOW/DENY recorded (params truncated, hash-chained)	-

E. Data-protection guardrails (DLP)

Capability	Detail
Modes	`redact` \| `block` \| `off` (`dlp_mode`)
Default block categories	`private_key`, `aws_access_key`, `jwt`
Detectors (10)	email, ssn, credit_card (Luhn-validated), phone, ipv4, aws_access_key, private_key, jwt, iban, secret_assignment
Consume path	Redact PII in records; block-mode can refuse
Produce path	Egress scan → `-32045`
Sensitive configs	Mask password/secret-like keys on describe (`redact_sensitive_configs`)
Scrub all outputs	Walk entire JSON result (`scrub_all_outputs`)
Legacy interceptor	`interceptor.redact_record` kept for compatibility; DLP is primary

F. Approval, taint & IFC

Capability	Detail
HMAC approval tokens	`mint` / `verify`; TTL (default 300s); tool-bound
Forged / expired tokens	Rejected → `-32042`
Default approval-required tools	`delete_topic`, `delete_records`, `create_acls`, `delete_acls`, `alter_partition_reassignments`, `alter_broker_config`
Taint guard	Best-effort substring match of session tainted values into destructive args
`ifc_strict`	After untrusted read, blocks destructive/control-plane without approval
Honesty	Taint is defeatable by laundering; least-privilege broker ACLs are load-bearing

G. Scoping, exposure & identity

Capability	Detail
Tool allow-list / deny-list	`tools_allowed`, `tools_denied`
Secure-by-default guidance	Shipped default is read/consume only; expand `tools_allowed` for mutate/destructive
Readonly mode	Disables create/produce/alter/delete/ACLs
Topic prefixes	`allowed_topic_prefixes`
Group prefixes	`allowed_group_prefixes`
Identity propagation	`identity_propagation` + in-memory per-principal ACLs (`set_principal_acl` / `authorize`)
Session identity	`session["identity"]` for audit, quarantine, ACL principal

H. Resilience & blast-radius controls

Capability	Detail
Circuit breakers	Per module: `data_plane`, `control_plane`, `ecosystem`
Breaker isolation	Control-plane open does not take down data-plane consume/produce
Dependency failure hook	`_inject_dependency_failure` / `_fail_module` → `-32043`
Rate limits	`rate_requests_per_second` + `rate_admin_requests_per_second`
Quarantine	`max_destructive_per_minute` per identity
Health resource	Breaker states on `kafka://health`

I. Consume semantics (Direct Partition Assignment)

Mode	Behavior
No `groupId`	`assignment=direct`, no consumer group, no rebalance
With `groupId`	Classic group path; register offsets; rebalance counter increments

J. Backend surface (in-memory Kafka)

create_topic, delete_topic, list_topics, describe_topic, alter_topic_config, produce, consume, list_groups, describe_group, group_lag, create_acls, list_acls, describe_cluster, principal ACLs, rebalance counter, dependency hooks.

K. Audit

Capability	Detail
Ring buffer	Recent entries (`maxlen=1000`)
Hash chaining	Tamper-resistant best-effort chain
Param truncation	Long params truncated (>64 chars)
Decisions	`ALLOW` / `DENY` with identity, tool, corr_id
Resource	`kafka://audit/recent`
`audit_topic`	Config name present; durable Kafka mirror is a documented reference gap

L. Error codes (complete - 15)

Code	Constant	Meaning
-32700	PARSE_ERROR	JSON parse error
-32600	INVALID_REQUEST	Invalid request
-32601	METHOD_NOT_FOUND	Unknown method/tool
-32602	INVALID_PARAMS	Invalid params / structured Kafka errors
-32603	INTERNAL_ERROR	Internal error
-32001	UNAUTHORIZED	Bad/missing bearer
-32029	RATE_LIMITED	Rate limited
-32040	TAINT_VIOLATION	Tainted value into destructive tool
-32041	SCOPE_VIOLATION	Topic/group out of scope
-32042	APPROVAL_REQUIRED	Destructive/sensitive needs approval
-32043	DEPENDENCY_UNAVAILABLE	Circuit breaker open / dependency down
-32044	POLICY_DENIED	Deny/allow/readonly/policy/ACL propagation
-32045	SENSITIVE_DATA_BLOCKED	Egress / DLP block
-32046	VALIDATION_FAILED	Malformed identifier / oversized value
-32047	QUARANTINED	Rogue-agent kill-switch

M. Configuration surface (32 fields)

bootstrap_servers, transport, tools_allowed, tools_denied, readonly, allowed_topic_prefixes, allowed_group_prefixes, taint_guard_enabled, approval_required_tools, dryrun_tools, audit_topic, policy_engine, circuit_breaker_enabled, dependency_timeout_ms, rate_requests_per_second, rate_admin_requests_per_second, oauth_expected_audience, oauth_expected_issuer, approval_signing_secret, redaction_enabled, dlp_mode, dlp_block_categories, scrub_all_outputs, redact_sensitive_configs, sensitive_topic_patterns, max_value_bytes, max_output_bytes, max_destructive_per_minute, ifc_strict, hard_max_records, hard_max_bytes, identity_propagation.

Full defaults: doc/configuration.md.

N. Quality, packaging & agent DX

Feature	Detail
Conformance suite	302/302 checks (incl. KIP Test Plan matrix + coverage gap-fill)
Line coverage	100% of `kafka_mcp/` via `python run_coverage.py` (optional `coverage` package)
Smoke + demo	`test_kafka_mcp.py` (16), `demo_end_to_end.py` (22 steps, all security codes)
Examples	Six folders with real-world `data/` fixtures
PyPI	`kafka-mcp-enterprise` · CLI `kafka-mcp-enterprise`
Stdlib-only core	No hard third-party deps
Optional OTel	`pip install …[otel]` - not required (doc/observability.md)
AGENTS.md + skills	Cursor / Kiro / Copilot / ChatGPT / Gemini (doc/agents-and-skills.md)

O. Documented reference gaps (intentional)

HTTP full server · real brokers · HTTP policy URL client · durable audit_topic publish · wall-clock dependency_timeout_ms · some approval tool names reserved but not all registered · production language = Java (this package is the Python reference). See doc/kip-alignment.md.

Quick start

Requires Python 3.8+. Core has no third-party packages.

# From source
python run_tests.py
python demo_end_to_end.py
python examples/01_sre_readonly_triage/run.py
echo {"jsonrpc":"2.0","id":1,"method":"tools/list"} | python serve_stdio.py

See the PyPI section at the top for pip install, or doc/publishing.md to publish a release.

Documentation & examples

Resource	Description
doc/	End-to-end guides: getting started, architecture, security, config, tools, errors, testing
doc/kip-alignment.md	Feature matrix vs KIP-1318 - what is implemented vs intentional reference gaps
doc/publishing.md	PyPI package `kafka-mcp-enterprise`
doc/observability.md	OpenTelemetry: optional, not required
doc/agents-and-skills.md	AGENTS.md + skills for all IDEs
examples/	Six folder-based scenarios (`run.py` + real-world `data/` fixtures)

Repository layout

flowchart TB
  root["kafka-mcp-enterprise-server"]
  pkg["kafka_mcp/<br/>reference server"]
  tests["tests/<br/>302-check suite"]
  docs["doc/<br/>guides + diagrams"]
  ex["examples/<br/>6 scenarios"]
  entry["serve_stdio.py · run_tests.py · demo_end_to_end.py"]
  root --> pkg
  root --> tests
  root --> docs
  root --> ex
  root --> entry

Engineering standards

This reference aims at production-grade practice even while staying a teaching implementation:

Practice	How it shows up
Fail-closed	First denial wins; no execute-then-check paths
Least privilege	Prefix scopes, allow/deny lists, readonly, approval for destructive ops
Defense in depth	MCP controls + explicit honesty that broker ACLs are load-bearing
Bounded blast radius	Hard record/byte caps, rate limits, per-plane breakers, quarantine
Observable denials	Stable error codes, correlation IDs, audit ALLOW/DENY
Testability	Deterministic in-memory backend; security + integration coverage
Zero dependency debt	Python stdlib only - easy to audit and run in CI
Clear product boundary	Official Kafka delivery tracked on KAFKA-20436 (Java)

Honest limitations (by design)

Taint / IFC is best-effort - defeatable by data laundering; do not treat as complete mediation.
In-memory Kafka - validates control logic; not a broker client.
stdio-first - HTTP is specified in the KIP; this reference documents notes, full HTTP is a Java/production concern.
Secure-by-default - shipped tools_allowed is read/consume only (SECURE_DEFAULT_TOOLS); enable mutate/destructive explicitly.

Note: Python reference vs Java production code

This repository is a Python reference implementation for validating the KIP-1318 security control model (stdio + in-memory backend + conformance tests). It is not written in Java and is not the official Apache Kafka MCP server.

Items listed as intentional gaps (Streamable HTTP / OAuth, live Admin-Producer-Consumer, EOS/fencing, Connect tools, distributed rate/audit state, JMX, and remaining tools/resources) are specified for the real implementation tracked under KAFKA-20436. Those belong in the actual Java production module (tools/mcp-server) and will be added there - not claimed as complete in this Python reference.

Full matrix: doc/kip-alignment.md.

KIP-1318 (wiki): https://cwiki.apache.org/confluence/display/KAFKA/KIP-1318%3A+Model+Context+Protocol+%28MCP%29+Server+for+Apache+Kafka
Jira: https://issues.apache.org/jira/browse/KAFKA-20436
MCP specification: https://modelcontextprotocol.io/

License & affiliation

Apache Kafka, KIP-1318, and KAFKA-20436 are trademarks / projects of the Apache Software Foundation. This repository is a community reference aligned with that proposal; it is not the official ASF deliverable unless and until merged under the Kafka project.

Available Tools

11 tools

alter_topic_configD

mutate / control_plane / ALTER

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must disclose behavioral traits. It merely implies mutation (mutate) but does not explain what happens, what permissions are needed, or any side effects. This is insufficient.

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 extremely short but not concise; it omits essential purpose information. Every sentence should earn its place, but here there is no real content.

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?

With no parameters and no output schema, the description should be complete on its own. It is not, as it does not explain the tool's role among siblings like delete_topic or describe_topic.

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?

Although there are zero parameters and schema coverage is 100%, the description should still add meaning. It fails to explain what 'alter_topic_config' does, providing no value beyond the empty schema.

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 'mutate / control_plane / ALTER' is a set of tags rather than a clear verb-resource pair. It does not specify what resource or configuration is being altered, making it nearly impossible to infer the tool's function.

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 guidance is provided on when to use this tool versus alternatives such as describe_cluster or create_topic. The description offers no context about prerequisites or typical usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

consume_messagesD

read / data_plane / READ

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.4/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, and the description provides no behavioral details such as whether consumption is blocking, if messages are deleted, or prerequisites. The text is essentially meaningless.

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 extremely short (three words) but is under-specified and not informative. It lacks structure and content that an agent needs.

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 critical nature of message consumption and the lack of output schema, the description is entirely inadequate. It does not explain what the tool returns, side effects, or how it interacts with the data plane.

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 is empty (0 parameters), so schema coverage is 100%. The description adds no parameter information, but with no parameters, the baseline is 3; however, it fails to add any useful context beyond the schema.

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 'read / data_plane / READ' is a tautology that restates the tool name without specifying what is consumed (messages? from where?) or the scope. It fails to distinguish from siblings like describe_topic or list_topics.

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 usage guidelines are provided. The description does not mention when to use consume_messages versus alternatives like produce_message or describe_consumer_group.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_aclsD

destructive / control_plane / ALTER

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description must disclose behavior. The term 'destructive' hints at modification, but lacks detail on permissions, reversibility, or side effects.

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?

Extremely concise, but at the cost of clarity. The brevity results in under-specification rather than efficient communication.

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 zero parameters, no output schema, and empty annotations, the description should compensate with rich behavioral context. It fails to provide enough information for an agent to understand the tool's function.

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?

No parameters in the schema, so schema coverage is 100%. Baseline score of 3 is appropriate; the description does not need to explain parameters but adds no additional value.

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 only provides tags 'destructive / control_plane / ALTER' without explicitly stating the tool creates ACLs. It is vague and does not clearly identify the resource or action, leaving the agent uncertain about its 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 guidance on when to use this tool versus siblings like alter_topic_config or describe_cluster. The description offers no context for appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_topicD

mutate / control_plane / CREATE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.3/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations and a vague description, the tool's behavior is entirely opaque. The word 'mutate' implies creation, but there is no disclosure of side effects, permissions, or error conditions (e.g., topic already exists).

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 extremely concise but at the expense of clarity. It uses only a few words, which is not an example of good conciseness because it omits essential information.

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 lack of parameters, output schema, and annotations, the description is the sole source of context. It fails to provide any meaningful information about the tool's purpose, behavior, or expected outcomes.

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 tool has zero parameters, so the schema provides full coverage trivially. However, the description adds no value; it does not explain what the tool does beyond the name. A baseline of 4 is not warranted because purpose is unclear.

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 'mutate / control_plane / CREATE' is essentially a tautology that restates the tool name without specifying the actual operation. It does not explain what creating a topic entails, nor does it distinguish from sibling tools like delete_topic or alter_topic_config.

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 guidance is provided on when to use this tool versus alternatives such as alter_topic_config or delete_topic. The description fails to indicate prerequisites, context, or scenarios where this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_topicC

destructive / control_plane / DELETE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

C2.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description includes 'destructive' which hints at mutation, but does not disclose what exactly happens (e.g., permanent deletion, impact on producers/consumers). With no annotations, the description carries full burden but provides minimal information.

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 extremely short but fails to convey essential purpose. Conciseness is not valuable when it omits necessary information.

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 simple tool with no parameters, the description should at least state 'Deletes a topic'. It does not, leaving the agent to infer from the name alone.

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?

No parameters exist, and schema coverage is 100% (empty schema). The description adds no param info, but baseline for zero params is 4. No extra value needed.

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 'destructive / control_plane / DELETE' is not a clear statement of what the tool does. It provides tags but not a verb+resource. The name suggests deletion, but the description is too vague to confirm.

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 guidance on when to use this tool versus alternatives like 'alter_topic_config' or 'create_topic'. No context on prerequisites or consequences.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

describe_clusterC

read / control_plane / DESCRIBE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

C2.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are empty, so description carries full burden. It hints at a read-only operation but provides no detail on what the output contains, side effects, or permissions. Barely meets minimum transparency.

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?

Extremely short and front-loaded, but lacks substance. Conciseness is appropriate for a simple tool, but the content is too cryptic to be fully useful.

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 tool's simplicity (no params, no output schema), a brief description could suffice, but 'read / control_plane / DESCRIBE' is insufficient. It fails to clarify the exact resource and behavior, leaving agents with ambiguity.

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?

No parameters, so the description doesn't need to add parameter semantics. Baseline of 4 applies as no information is missing regarding parameters.

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?

Description states 'read / control_plane / DESCRIBE', which indicates a read operation on the control plane but doesn't explicitly say what is being described (presumably the cluster). It distinguishes from siblings like describe_topic only by the resource, but it's vague.

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 guidance on when to use this tool vs alternatives. No mention of prerequisites, context, or comparison to sibling tools like describe_topic or list_topics.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

describe_consumer_groupD

read / control_plane / DESCRIBE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations present, the description carries the full burden of disclosing behavioral traits. It only states 'read', which hints at read-only access but does not confirm safety, permissions, or side effects. No information about what gets returned or any constraints is provided.

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 extremely short but lacks necessary detail, resembling a tag rather than a usable description. While conciseness is valued, this is under-specification that hinders understanding.

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 complexity of describing a consumer group in Kafka and no output schema, the description is completely inadequate. It does not mention what information is returned (e.g., offsets, members, state) or any behavioral expectations.

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?

Despite having zero parameters (schema coverage 100%), the description fails to add meaning beyond the schema. The cryptic label 'read / control_plane / DESCRIBE' does not explain what consumer group is being described or how the tool operates without any input.

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 'read / control_plane / DESCRIBE' is tautological, merely repeating the verb from the name and providing no specific information about what the tool does or what resource it acts on. It does not clarify that a consumer group is being described, nor does it differentiate from sibling tools like list_consumer_groups.

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 description offers no guidance on when to use this tool versus alternatives such as describe_topic or list_consumer_groups. There is no mention of prerequisites, context, or excluded scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

describe_topicD

read / control_plane / DESCRIBE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.7/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description bears full responsibility. It only states 'read' which implies a safe operation, but gives no additional behavior details (e.g., whether it requires authentication, what it returns, or if it has side effects).

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 extremely short but under-specifying. It is not informative, consisting only of a phrase that adds no new insight beyond the name.

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?

Even with zero parameters and no output schema, the description fails to explain what the tool returns or how it behaves. A minimal tool still needs a sentence clarifying its output.

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?

With no parameters and 100% schema coverage, the baseline is 3. The description adds no parameter information, but the schema already covers everything (empty). No credit for adding meaning.

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 repeats the tool's purpose with 'read / DESCRIBE' but does not specify what resource is described (e.g., which topic, or whether it describes a single topic or all topics). It adds no specific verb-resource pairing beyond the name.

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 guidance on when to use this tool versus siblings like describe_cluster or describe_consumer_group. No conditions, prerequisites, or exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_consumer_groupsD

read / control_plane / DESCRIBE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.4/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No behavioral traits are disclosed. The description does not indicate that this is a read-only operation or any potential side effects. Annotations are empty, so the description carries the full burden.

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 extremely short but not front-loaded with useful information. It lacks structure and clarity, making it under-specified rather than 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?

Given no output schema and no annotations, the description is far too minimal. It fails to explain what the tool returns or how it behaves, leaving the agent without critical context.

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?

With zero parameters and 100% schema coverage, the baseline is 3. The description adds no additional meaning beyond the schema, but no parameter information is needed.

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 'read / control_plane / DESCRIBE' is cryptic and does not clearly state that the tool lists consumer groups. It fails to convey the tool's core function in plain language.

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 guidance is provided on when to use this tool versus alternatives like describe_consumer_group or list_topics. The description lacks any usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_topicsD

read / control_plane / DESCRIBE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.9/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must disclose behavioral traits. It does not mention anything about read-only nature, permissions, or output behavior. The cryptic text offers no transparency.

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?

While very short, the description is not clear. It uses a cryptic format that confuses rather than clarifies. Conciseness is good only when it retains clarity, which is not the case here.

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 no annotations, no output schema, and a simple tool, the description fails to provide essential context. It doesn't explain what the tool lists, what it returns, or any usage notes. Completely insufficient.

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 is empty (0 parameters), so schema description coverage is trivially 100%. The description adds no extra meaning, but there is nothing to add. Baseline of 3 is appropriate as no additional parameter context is needed.

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 'read / control_plane / DESCRIBE' is cryptic and does not explicitly state that this tool lists topics. The name 'list_topics' hints at the purpose, but the description adds little clarity and could be considered a tautology.

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 versus sibling tools like describe_topic or create_topic. The description provides no context for appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

produce_messageD

mutate / data_plane / WRITE

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.3/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, and the description only says 'WRITE', which implies mutation but offers no details about side effects, idempotency, or authorization requirements. The description fails to disclose behavioral traits.

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 extremely short but lacks substance. Every word should earn its place; here they do not convey useful information.

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 no output schema, no annotations, and a vague description, the tool is severely underspecified. An agent cannot understand what the tool does, making it incomplete.

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?

With zero parameters and 100% schema coverage, the baseline is 4, but the description adds no meaning beyond the empty schema. It does not explain what the tool produces or requires, so the agent gains no insight.

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 'mutate / data_plane / WRITE' is vague and does not specify what the tool produces or to what resource. It fails to distinguish from siblings like 'consume_messages' or 'alter_topic_config'.

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 guidance on when to use this tool versus alternatives. No context about prerequisites or usage scenarios is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

11 tool updatesv0.1.0Jul 28, 2026
- First observedalter_topic_config
- First observedconsume_messages
- First observedcreate_acls
- First observedcreate_topic
- First observeddelete_topic
- First observeddescribe_cluster
- First observeddescribe_consumer_group
- First observeddescribe_topic
- First observedlist_consumer_groups
- First observedlist_topics
- First observedproduce_message

Deploy Server

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TDQS

C2.4/5.0

Scored 2026-07-28 19:55 across 11 tools

Disambiguation5/5

Each tool targets a distinct Kafka resource or action (e.g., cluster, topic, consumer group, ACL, message). There is no overlap in purpose, and the descriptions clearly differentiate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., describe_cluster, create_topic, consume_messages). No deviations or mixed conventions.

Tool Count5/5

11 tools cover the essential operations for a Kafka enterprise server: cluster info, topic CRUD, consumer groups, ACLs, and message production/consumption. The scope is well-balanced.

Completeness4/5

Covers most core Kafka lifecycle operations, but missing tools for deleting or listing ACLs and possibly deleting consumer groups. These are minor gaps for an enterprise server.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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Kafka MCP Enterprise Server

Implemented vs Spec-only

Website

PyPI

Agents & skills (Cursor, Kiro, ChatGPT, Gemini, Copilot, …)

Why this exists

What’s included

How it runs

Fail-closed pipeline (overview)

Complete features, security controls & guardrails

A. MCP protocol & surface

B. Tools (13) - classified

C. Resources (kafka://)

D. Fail-closed security pipeline (exact order)

E. Data-protection guardrails (DLP)

F. Approval, taint & IFC

G. Scoping, exposure & identity

H. Resilience & blast-radius controls

I. Consume semantics (Direct Partition Assignment)

J. Backend surface (in-memory Kafka)

K. Audit

L. Error codes (complete - 15)

M. Configuration surface (32 fields)

N. Quality, packaging & agent DX

O. Documented reference gaps (intentional)

Quick start

Documentation & examples

Repository layout

Engineering standards

Honest limitations (by design)

Note: Python reference vs Java production code

Related links

License & affiliation

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

C. Resources (`kafka://`)