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CKBrennan

overtone-news-mcp

tone

Analyze recent news coverage of a topic to reveal emotional tone distribution and dominant sentiment. Understand public mood with tones like happy, angry, or fearful.

Instructions

Get the emotional tone distribution across recent coverage of a topic (happy, funny, hopeful, informational, angry, sad, fearful) plus the dominant_tone. Use when the user asks how a topic is being talked about or the public mood around it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool handler function for 'tone'. It accepts a query and days parameter, then delegates to _post('tone', ...) which calls the Overtone News API.
    @mcp.tool()
    def tone(
        query: Query,
        days: Annotated[int, Field(ge=1, le=10)] = 3,
    ) -> dict[str, Any]:
        """Get the emotional tone distribution across recent coverage of a
        topic (happy, funny, hopeful, informational, angry, sad, fearful)
        plus the dominant_tone. Use when the user asks how a topic is being
        talked about or the public mood around it."""
        return _post("tone", {"query": query, "days": days})
  • The @mcp.tool() decorator registers the `tone` function as an MCP tool.
    @mcp.tool()
  • Type aliases used in the tone tool: ToneFilter (positive/negative/informational) and ToneName (happy, funny, hopeful, informational, angry, sad, fearful).
    ToneFilter = Literal["positive", "negative", "informational"]
    ToneName = Literal[
        "happy", "funny", "hopeful", "informational", "angry", "sad", "fearful"
    ]
  • The _post helper makes an authenticated POST request to the Overtone API, used by the tone handler to call the 'tone' endpoint.
    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()
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that it returns tone distribution and dominant_tone, and implies a read operation via 'Get'. However, it does not detail behaviors such as handling empty results, rate limits, or mutation safety. The description is adequate but not comprehensive.

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 two sentences: the first defines the tool's action and output, the second provides usage guidance. Every sentence adds necessary value, and it is front-loaded with the core purpose. No redundant words.

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?

Given the tool has 2 parameters, one required, and an output schema (not provided), the description explains what the tool returns and when to use it. It lacks explicit parameter details but otherwise covers key aspects. The output schema likely fills return format details.

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 description coverage is 0%, so the description must compensate. It mentions 'recent coverage' which hints at the 'days' parameter, and 'topic' relates to 'query'. However, it does not explicitly define what 'query' or 'days' mean, their format, or constraints. The description adds some context but insufficiently for the 0% coverage.

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 the tool retrieves emotional tone distribution across recent coverage of a topic, listing specific tones. It distinguishes itself from siblings by providing a usage context: 'Use when the user asks how a topic is being talked about or the public mood around it.'

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 explicitly states when to use the tool ('Use when the user asks how a topic is being talked about or the public mood around it'), providing clear context. It does not explicitly mention when not to use or list alternatives, but the context is sufficient for an AI agent.

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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