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CKBrennan

overtone-news-mcp

velocity

Identify which news concepts experienced the sharpest tone shifts between the last 48 hours and the most recent 24 hours, revealing where narratives are turning.

Instructions

Concepts whose tone distribution shifted the most sharply between the prior 48h and the most recent 24h. Useful for 'where is the narrative turning?' questions. Ranked by shape-normalized L2 distance, so a uniform volume rise doesn't count as a shift.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The velocity MCP tool function. Accepts a limit parameter (1-100, default 10) and delegates to the _post helper with the 'velocity' API endpoint.
    @mcp.tool()
    def velocity(
        limit: Annotated[int, Field(ge=1, le=100)] = 10,
    ) -> dict[str, Any]:
        """Concepts whose tone distribution shifted the most sharply
        between the prior 48h and the most recent 24h. Useful for
        'where is the narrative turning?' questions. Ranked by
        shape-normalized L2 distance, so a uniform volume rise
        doesn't count as a shift."""
        return _post("velocity", {"limit": limit})
  • Registration of the velocity function as an MCP tool via the @mcp.tool() decorator on the FastMCP instance.
    @mcp.tool()
  • The _post helper function that velocity() calls. Sends an HTTP POST to the Overtone API with the X-API-Key header and JSON body, returning the parsed response.
    def _post(path: str, body: dict[str, Any]) -> dict[str, Any]:
        with httpx.Client(timeout=HTTP_TIMEOUT) as client:
            resp = client.post(
                f"{API_URL}/{path}",
                headers={"X-API-Key": _load_api_key()},
                json=body,
            )
            resp.raise_for_status()
            return resp.json()
  • Input schema for the velocity tool: a single integer 'limit' parameter constrained between 1 and 100, defaulting to 10.
        limit: Annotated[int, Field(ge=1, le=100)] = 10,
    ) -> dict[str, Any]:
Behavior4/5

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

With no annotations, the description carries full burden. It discloses time windows, ranking metric (shape-normalized L2 distance), and behavior caveat (uniform volume rise doesn't count). It is transparent about how the tool works, though it could mention it is read-only.

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 three sentences, front-loaded with the core action, then adding use case and technical nuance. Every sentence adds value, and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (not shown), return values need not be explained. The description covers input, logic, and use case comprehensively. No missing elements.

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 description should compensate, but it does not mention the 'limit' parameter at all. While the parameter is simple (integer with defaults), the lack of any description means the agent must infer its purpose from context. This is a gap.

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 that the tool identifies concepts with the most significant shifts in tone distribution between two time windows (prior 48h vs most recent 24h), making the purpose obvious. It also explains the ranking metric, distinguishing it from sibling tools like 'emerging' or 'tone'.

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 provides a clear use case ('where is the narrative turning?') but does not explicitly compare to alternatives. However, the context signals and sibling tool names imply differentiation; the description offers enough guidance for informed selection.

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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