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Kingfisher Streaming Inference

Kingfisher Streaming Inference

by J-X0
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

stream_stats

Retrieve running statistics for a stream to monitor inference activity and validate model performance as new events are scored.

Instructions

Return running statistics for a stream.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "stream": "<stream>"
    },
    "name": "stream_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
streamYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations, the description must carry the behavioral disclosure burden. The verb 'Return' suggests a read-only operation, but nothing is said about side effects, live versus historical data, the meaning of 'running', error conditions, or whether the stream must exist. This is minimal 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?

The sentence is short and free of filler, so it is concise. But the extreme brevity under-specifies the tool's meaning and context, making it less useful than a slightly longer, structured description would be.

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?

The description is too thin for a tool with no output schema, no annotation, and one undocumented parameter. There is no return-value shape, no parameter guidance, and no relationship to sibling tools, so an agent cannot confidently invoke it correctly.

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% and the description does not define the single 'stream' string parameter beyond repeating the noun. It does not specify whether 'stream' is a name, ID, ARN, or what format is expected, so an agent must guess the input semantics.

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 uses a specific verb ('Return') and names a resource ('running statistics for a stream'), which distinguishes it from the audit/ingest/export siblings. However, 'running statistics' and 'stream' are not defined, so it falls short of a fully specific, unambiguous purpose statement.

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 explicit guidance is given about when to use this tool versus the sibling tools. The phrasing implies a stats-retrieval use case, but there are no exclusions, alternative references, or contextual cues to help the agent decide among the available tools.

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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