Sliq

Google News Search

google_news_search

Read-only

A find-companies News-mode discovery source, used alongside search_news_events, tavily_search, and exa_search_news — the News-mode skill owns the routing (which signals fan out here). Its edge is Google's local and trade-press index: hyper-local expansion stories, plant incidents within hours of publication, and small-market executive moves the other sources' indexes miss. Returns raw articles, not company rows — extract the company from each article's title/snippet the same way as tavily_search results.

Compose KEYWORD-style queries — the terms a journalist's headline would contain, e.g. 'appoints new chief marketing officer' or 'breaks ground manufacturing facility'. Google matches keywords, not meaning: full-sentence semantic phrasing ("A company that recently...") returns ZERO results. Geographic scoping via keywords is brittle (a location term matches any article containing the word, wherever the company is) — prefer scoping by event keywords and filtering location downstream. One query per signal, all in one call (the tool fans them out in parallel; one tool call is one LLM round).

Expect the same story to arrive several times from different outlets — Google clusters dilute a page's effective yield to ~5–7 distinct companies. Dedup is on exact URL here; cross-outlet same-story dedup happens downstream at record_search_results.

Articles are recency-bounded server-side by Google's coarse day/week/month/year buckets, using the smallest bucket covering lookback_days (default 30) — a bucket can exceed the window, so also check each row's published_at when the window matters. A news scan passes the window from its trigger prompt.

Costs 0.05 Sliq credits per query that returns without error; a failed query is free. Companies you record from its articles are not charged. Soft-fails on insufficient credits: the charge is capped at the remaining balance. Dict with articles array and count. Each article row is {title, url, source, snippet, date, published_at, query} — source is the publisher name, date is Google's relative publish string ("2 days ago"), published_at is a coarse UTC ISO estimate derived from it (None when unparseable), and query names the originating query. Rows are deduped by URL across queries.

If every query failed with an upstream error and nothing landed, also google_news_available: False plus a human-readable error — a source outage, NOT a genuinely empty feed. Partial failure (some queries errored, others returned) stays silent and reports only the articles.

On insufficient Sliq credits the call is refused before running — it raises InsufficientCreditsError instead.

Input Schema

TableJSON Schema

Name	Required	Description
`queries`	Yes	One keyword-style news query per signal. Pass a list of one for a single-signal lookup.
`on_behalf_of`	No	An agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account.
`lookback_days`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 19, 2026

addedInput schema / properties / on_behalf_of

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "An agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."
+}

First observedSep 15, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With only readOnlyHint=true in annotations, the description carries rich extra context: cost (0.05 Sliq credits per successful query, free on failure), soft-fail/capped charge on insufficient credits, server-side recency bucketing that can overrun lookback_days, URL-only dedup with clustering dilution to ~5-7 companies, and silent partial failures. This is exactly the behavior an agent needs beyond the safety hint.

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?

Length is substantial but every section is front-loaded and earns its place (query construction, dedup expectation, recency semantics, cost model, returns). Structured with summary/returns tags rather than a wall of prose. Slightly verbose for the conciseness bar, but no filler sentences.

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?

No output schema exists, and the description fully supplies the return contract (article row shape and field meanings), plus failure semantics: google_news_available:False with error on total outage, silent partial failure, and InsufficientCreditsError refusal. Nothing an agent needs to call or interpret results is missing.

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

Parameters4/5

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

Schema coverage is 67% and the description compensates materially: it explains query composition (keyword-style, one per signal, fanned out in parallel, full-sentence phrasing returns zero) and documents the semantics of lookback_days (smallest day/week/month/year bucket covering it, default 30). on_behalf_of is left to the schema, which already documents it well.

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+resource ('Discover companies from recent news via Google News keyword search') and explicitly positions itself against named siblings (search_news_events, tavily_search, exa_search_news). It even articulates its distinctive edge over those sources (local and trade-press index coverage).

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?

Names the alternatives it is used alongside and explains the condition that favors it ('hyper-local expansion stories, plant incidents within hours, small-market executive moves the other sources' indexes miss'). It also gives clear operational guidance (prefer event keywords, scope location downstream). It stops short of an explicit when-not rule, deferring routing to the 'News-mode skill'.

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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