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CKBrennan

overtone-news-mcp

emerging

Detect concepts that appeared in the last 24 hours with zero coverage in the prior 48 hours, filtered to at least 3 articles from 2 sources, to surface candidate emerging stories.

Instructions

Concepts that appeared in the last 24h but had zero coverage in the prior 48h — candidate emerging stories. Cluster-filtered to

=3 articles and >=2 sources to suppress single-article noise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler function for the 'emerging' tool. Accepts an optional 'limit' parameter (1-100, default 10) and delegates to the _post helper to call the Overtone API's 'emerging' endpoint. Returns concepts that appeared in the last 24h with zero prior coverage.
    @mcp.tool()
    def emerging(
        limit: Annotated[int, Field(ge=1, le=100)] = 10,
    ) -> dict[str, Any]:
        """Concepts that appeared in the last 24h but had zero coverage in
        the prior 48h — candidate emerging stories. Cluster-filtered to
        >=3 articles and >=2 sources to suppress single-article noise."""
        return _post("emerging", {"limit": limit})
  • Registered as an MCP tool via the @mcp.tool() decorator on line 142.
    @mcp.tool()
  • The _post helper function that all tools (including emerging) use to make HTTP POST requests to the Overtone API, handling API key loading and JSON serialization.
    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 'emerging' tool: 'limit' is an integer between 1 and 100, with a default of 10. Uses Pydantic's Field for validation. The return type is dict[str, Any].
    limit: Annotated[int, Field(ge=1, le=100)] = 10,
Behavior4/5

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

No annotations provided, so the description adequately covers behavioral traits: time constraints (last 24h vs prior 48h), clustering rules (>=3 articles, >=2 sources). Could mention output format, but output schema covers that.

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?

Extremely concise: two sentences deliver purpose, time windows, and filters without fluff. Front-loaded with the key action.

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?

Description covers core logic and filtering, but omits explanation of the 'limit' parameter. Output schema likely fills in return values. Minor gap prevents a 5.

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 only parameter ('limit') is not described in the text. Schemas have 0% coverage, so the description should explain its purpose. The default and constraints are in the schema, but no added value.

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 defines what the tool does: identifies concepts appearing in the last 24 hours with zero prior coverage, filtered to suppress noise. It specifies the time windows and clustering criteria, making its purpose unmistakable.

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 finding emerging stories but does not contrast with sibling tools like 'news' or 'timeseries'. No explicit guidance on when to use this tool vs 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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