Sliq

Search News Events

search_news_events

⚠️ DISCOVERY ONLY — NOT a company lookup. This returns a global feed of the most recent events matching categories + company_locations (deduped by company domain, ~10 rows). It takes NO company / domain / name input and CANNOT be scoped to a specific company or to a list of known companies. Use it ONLY for find-companies discovery ("surface net-new companies that just did X"). To research signals for a SPECIFIC named company or an uploaded prospect/account list, this is the WRONG tool — use tavily_search with a company-scoped query (e.g. " customer success news 2026") instead. search_monitor_events(company_domain=...) works too, but only for companies the user already monitors.

Use this when the user asks to DISCOVER companies by a signal that maps to one of the supported categories below. Only fall back to tavily_search if this returns no results or the signal isn't covered.

Costs 0.3 Sliq credits per event record PredictLeads returns, up to 10 records per location (so up to 3 credits per location). A location query that fails is free.

When this runs in an agent, every event company with a domain is saved to the Output tab as a company row (deduped by domain) carrying the event in data.signals. To filter them, record a verdict on each saved row with record_search_results, under the domain it was saved with.

Supported categories (pass one or more in a single call): Expansion: expands_facilities, expands_offices_in, expands_offices_to, opens_new_location, increases_headcount_by, attends_event Investment: receives_financing, goes_public, invests_into, invests_into_assets, has_earnings, has_revenue, has_valuation Leadership: hires, leaves, promotes, retires_from Contract: signs_new_client, loses_client Cost cutting: decreases_headcount_by, closes_offices_in New offering: launches, is_developing, integrates_with Partnership: partners_with, ends_partnership_with Acquisition: acquires, merges_with, sells_assets_to Challenges: declares_bankruptcy, files_suit_against, has_issues_with Recognition: receives_award, recognized_as Relational: identified_as_competitor_of, spins_off_company, spins_off_division Dict with events array, count, and created_identifiers (the domains this call newly added to the list). If every location-query failed with an upstream error (timeout / 5xx) and nothing landed, also predictleads_available: False plus a human-readable error — a source outage, NOT a genuinely empty feed. Partial success (some locations errored, others returned events) stays silent and reports only the events.

Input Schema

TableJSON Schema

Name	Required	Description
`agent_id`	No	Optional — a specific agent to save the companies into. Omit it to use the running agent, which is the usual case.
`list_name`	No	Short kebab slug naming the Output-tab list bucket. Absent, companies land in the 'default' list.
`categories`	Yes	One or more category names from the enumerated list above (the parameter is constrained to exactly those values). Map common intents to the right name: "funding" → receives_financing, "IPO" → goes_public, "acquisition" → acquires, "layoffs" → decreases_headcount_by. Pass all relevant categories in a single call — they are fetched together at no extra cost.
`lookback_days`	No	Days of news history to scan (default 30). A news scan passes the window from its trigger prompt.
`company_locations`	No	Filter by country or US state. Pass a list to cover multiple locations (e.g. ["United States", "Canada"]). At most 10 API requests are made across all locations. Omit to search globally. Prefer country-level values (e.g. "United States") over individual cities/states unless the ICP is tightly city-scoped.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 30, 2026

addedInput schema / properties / agent_id

Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Optional — a specific agent to save the companies into. Omit\nit to use the running agent, which is the usual case."
+}

addedInput schema / properties / list_name

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Short kebab slug naming the Output-tab list bucket. Absent,\ncompanies land in the 'default' list."
+}

First observedSep 15, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations only declare readOnlyHint=false / destructiveHint=false, and the description goes far beyond them: it discloses credit cost per event record with a per-location cap, that failing location queries are free, that companies are auto-saved to the Output tab deduped by domain, and that total upstream failure surfaces predictleads_available=False rather than an empty feed. That is exactly the side-effect, cost, and failure-mode context annotations cannot carry.

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-loaded with the summary and the critical discovery-only warning before any detail, with clear sectioning. Slightly long because the full category taxonomy is reproduced from the enum in the schema, which is mild duplication, but each section still earns its place.

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?

Covers purpose, alternatives, cost, side effects, downstream workflow (recording verdicts via record_search_results), failure semantics, and return shape via its <returns> block. For a 5-parameter discovery tool with no output schema, nothing an agent needs to call it correctly 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 100%, so baseline would be 3, but the description adds real meaning on top: it maps common intents to category names (funding→receives_financing, layoffs→decreases_headcount_by), instructs batching all categories in one call at no extra cost, and constrains company_locations to prefer country-level values with a 10-request cap. Only agent_id/list_name are left to the 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+resource ('Discover recent news events across ALL companies, filtered by category') and immediately scopes it as global discovery rather than a company lookup. It explicitly names the two siblings it is not (tavily_search, search_monitor_events) so the agent can differentiate without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ('DISCOVER companies by a signal that maps to one of the supported categories'), when-not ('To research signals for a SPECIFIC named company ... this is the WRONG tool'), and names concrete alternatives with the condition that selects each. Nothing is left to inference.

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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