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news-mcp

by danmarce
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

compare_coverage

Read-only

Compare how different news outlets or countries covered a story to reveal framing differences and identify silent omissions.

Instructions

How different outlets/countries covered one story - for framing comparison ("compare how the German, Qatari, US and Ecuadorian press covered X"). Returns matching items grouped by outlet, plus the active outlets with NO matching item (silence can be telling, but may also just mean the keywords were in another language).

    Same matching as `news_by_topic`: include the key term in every relevant language separated by
    "|", e.g. "Ecuador | Équateur | エクアドル | الإكوادور".
    When answering, compare emphasis, word choice and what each outlet includes or omits, citing each
    outlet by name; translate faithfully and do not inject your own view.

    Args:
        topic: keywords; spaces = AND, "|" = OR between alternatives.
        days: look-back window in days (default 7).
        per_source: max items per outlet (default 3, max 10).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
topicYes
per_sourceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, it discloses non-obvious behavior: results come back grouped by outlet, and active outlets with no matching item are returned too, with the caveat that 'silence' may simply reflect keyword/language mismatch. It also warns that key terms must be supplied in every relevant language, which directly shapes correct invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the core purpose and return behavior, then the matching rules and Args, which is a sensible ordering. The 'When answering...' paragraph is useful but is answer-style guidance rather than tool-invocation guidance, so it is slightly off-target and lengthens the description.

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?

With no output schema, the description must explain what comes back, and it does: matching items grouped by outlet plus outlets with no matches. Combined with the parameter documentation and language caveat, an agent has everything needed to call it and interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden and does: topic is documented with AND ('spaces') and OR ('|') semantics plus a multi-language example, days is the look-back window with default 7, and per_source is capped at 10 with default 3. Every parameter gains meaning not present in the bare schema.

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?

States a specific verb and resource ('compare how different outlets/countries covered one story') and immediately frames the use case as framing comparison with a concrete example query. It also distinguishes itself from the sibling news_by_topic by noting it uses the same matching but groups results per outlet, so an agent can tell the two apart without opening a schema.

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?

Provides a clear usage scenario and an example phrasing, plus points to news_by_topic as the related matcher, which implicitly routes single-topic searches elsewhere. It does not explicitly state when not to use this tool (e.g., when no cross-outlet comparison is wanted), so it falls short of a full when/when-not/alternatives statement.

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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