Provides adaptive audio jitter buffering and packet loss concealment for real-time voice streams, ensuring smooth playout over volatile network conditions.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-audio-packet-jitter-resilience-buffer-skill smooth this audio frame using adaptive jitter buffering"
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.
Packet ordering simulation and inter-arrival jitter telemetry.
This is a packet metadata simulation, not a WebRTC implementation or audio codec. Timestamps must already be converted to milliseconds, and sequence numbers must be monotonically extended (no RTP wraparound). Missing packets produce markers, not synthesized audio. The caller controls playout timing; target_delay_ms is telemetry, not an enforced timer.
Python 3.9 or newer. The library and stdio MCP server have no runtime dependencies.
python -m pip install https://github.com/Alpha-Park/genpark-audio-packet-jitter-resilience-buffer-skill/releases/download/v1.0.1/genpark_jitter_buffer-1.0.1-py3-none-any.whlPyPI publication is pending account setup. The intended PyPI project is genpark-jitter-buffer;
do not assume pip install genpark-jitter-buffer is available until the project is published.
Related MCP server: genpark-multimodal-audio-emotion-valence-detector-skill
from genpark_jitter_buffer import AudioPacketJitterResilienceBuffer
client = AudioPacketJitterResilienceBuffer()
print(client.run_benchmark_jitter_simulation())After installing the wheel, configure your MCP client with the installed command:
{
"mcpServers": {
"genpark-jitter-buffer": {
"command": "genpark-jitter-buffer",
"args": []
}
}
}If the command is not on PATH, use its absolute path or python -m genpark_jitter_buffer
with the same interpreter where you installed the wheel.
The GitHub release also contains a .mcpb bundle for clients supporting desktop extensions.
That bundle requires a Python 3.9+ interpreter on PATH; it bundles the server source.
Available tools: ingest_packet, extract_playout_frame, get_jitter_telemetry, run_benchmark_jitter_simulation.
tools/list returns required arguments and JSON schemas.
Each MCP process holds its own state. Benchmark tools use isolated instances.
python -m unittest discover -s tests
python -m pip install mcp
python tests/check_mcp.py
python -m pip install build twine
python -m build
python -m twine check dist/*python mcp_server.py --test runs the deterministic example; it is not a protocol conformance test.
The MCP client check exercises initialize, tools/list, tools/call and ping over stdio.
GitHub source and release artifacts are the primary distribution until PyPI is configured. Registry submissions are tracked separately; a manifest is not proof of registry acceptance. See PUBLISHING.md for the repeatable PyPI workflow.
MIT license. Maintained by GenPark.
extract playout frame
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full burden of behavioral disclosure, and it discloses nothing: no safety profile, no side effects, no auth/permission requirements, no output or return behavior for what is presumably an extraction operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
It is short and front-loaded, but this is under-specification rather than conciseness — one sentence that simply echoes the name earns no informational value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a bare zero-param tool the description still must explain what 'playout frame' means and what the caller receives, since there is no output schema and no annotations. It explains none of that, leaving the agent unable to judge results or purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is no parameter semantics for the description to clarify; the baseline of 4 applies. Nothing here is misleading, but nothing is added either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description is a verbatim restatement of the tool name ('extract playout frame') with no added scope, target, or differentiation from siblings like ingest_packet or get_jitter_telemetry. An agent learns nothing about what a 'playout frame' is or what extraction produces.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance, no alternatives, no preconditions, no mention of the sibling tools. The description offers zero routing information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get jitter telemetry
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, and it discloses nothing: not the data source, time window, sampling behavior, or whether it is a read-only operation. 'get' weakly implies a read but that is inference, not disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three words, front-loaded but purely a name echo. This is under-specification rather than earned conciseness — nothing in the sentence adds information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
A parameterless tool with no output schema has a low structural bar, but the description still says nothing about what telemetry is returned or how it relates to the sibling simulation and ingestion tools. The agent is left to guess at the tool's role.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero parameters, so there are no parameter semantics to document; per the baseline rule a parameterless tool lands at 4. No additional detail in the description could compensate for anything here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'get jitter telemetry' merely restates the tool name verbatim with no added scope, no verb specificity beyond 'get', and no distinction from siblings like run_benchmark_jitter_simulation. An agent cannot tell what the telemetry covers or how it differs from the simulation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no when-to-use or when-not-to-use guidance at all. Nothing tells the agent whether to call this instead of run_benchmark_jitter_simulation or how the two relate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ingest packet
| Name | Required | Description | Default |
|---|---|---|---|
| seq_num | Yes | ||
| arrival_ms | Yes | ||
| payload_bytes | Yes | ||
| rtp_timestamp | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden, and it discloses nothing. It is unclear whether this mutates simulation state, buffers a packet, requires prior initialization, or is idempotent – critical unknowns for a tool taking four required packet-timing fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The two-word description is short but by under-specification rather than conciseness; there is no front-loaded purpose or supporting detail to earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
A four-parameter, fully-required tool with no annotations, no output schema, and 0% parameter documentation is completely inadequate. An agent has no basis to construct a correct call or predict effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% across four required parameters (seq_num, rtp_timestamp, payload_bytes, arrival_ms), and the description adds no meaning for any of them. Acceptable ranges, units (ms vs ticks), and ordering constraints are entirely undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description is a verbatim tautology of the tool name 'ingest_packet' – it restates 'ingest packet' with no additional specificity. It does not identify what a packet is in this domain (RTP/jitter simulation, per the siblings) or what ingesting it accomplishes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance whatsoever on when to call this versus siblings like get_jitter_telemetry or extract_playout_frame. No prerequisites, ordering, or state requirements are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run benchmark jitter simulation
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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, and it discloses nothing. It does not say whether the simulation mutates state, what inputs it consumes given zero parameters, how long it runs, or what side effects it produces. The generic verb 'run' gives no behavioral signal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short phrase and thus not bloated, which is its only merit. However, that brevity reflects under-specification rather than disciplined concision, since no useful content was trimmed to achieve it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simulation tool with no annotations and no output schema, the description should explain what the benchmark does and what it returns. It supplies none of this, leaving the agent unable to predict behavior or results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is nothing for the description to add. Per the rubric, a zero-parameter tool establishes a baseline of 4 on this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description merely restates the tool name verbatim ('run benchmark jitter simulation'), adding no scope, output, or distinguishing detail. It does not differentiate this tool from siblings like get_jitter_telemetry, which also concerns jitter. This is a tautology rather than a clarified purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as get_jitter_telemetry or extract_playout_frame. No preconditions, exclusions, or triggering context are stated. The agent is left to infer usage entirely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.1extract_playout_frameget_jitter_telemetryingest_packetrun_benchmark_jitter_simulationScored across 4 tools
Each tool targets a distinct action on the pipeline: ingest (input), extract (output), telemetry (observability), and benchmark (testing). There is no meaningful overlap; an agent can unambiguously pick the right tool for each stage.
All four tools follow a strict verb_noun snake_case pattern (ingest_packet, extract_playout_frame, get_jitter_telemetry, run_benchmark_jitter_simulation). Conventions are predictable and readable throughout.
Four tools is reasonable for a narrow audio jitter buffer skill, covering the core ingest/playout/telemetry/benchmark flow. It is slightly thin, but every tool earns its place with no filler.
The ingest-to-playout pipeline, telemetry, and benchmarking are covered, but there is no way to configure buffer depth, reset/flush state, or query buffer status. These are notable gaps for a jitter resilience buffer that agents may need to work around.
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