Industry Pulse

Get the latest AI coding agent events

latest_events

Read-onlyIdempotent

List recent events in AI coding agents, ranked by score and heat. Use it for questions like "what is new in AI coding agents?", "what happened today?", "any big launches this week?". Optional: hours to look back (default 24, at most 168), limit (default 10, at most 25). Returns up to limit events, each with a title, a short summary, a category, a 0 to 10 score, a heat count of independent sources within 48 hours, and links to those sources with their dates, plus asOf, the time the sources were last read. The same event reported by several outlets is one event.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "latest_events"
  }
}

Input Schema

Name	Required	Description
`hours`	No	Look back this many hours (default 24, at most 168)
`limit`	No	Most events returned (default 10)
`niche`	No	The topic area to read. Only "ai-coding-agents" (AI coding agents) exists today, and it is the default.

Output Schema

Name	Required	Description
`asOf`	Yes	When the sources were last read completely
`note`	Yes
`as_of`	Yes	ISO 8601 time the data was published by its source or fetched from it. A cached answer keeps the time of its fetch.
`niche`	Yes
`total`	Yes	Matching events before the limit was applied
`events`	Yes
`source`	Yes	Who the data comes from, to cite and to check it against.
`untrusted_source`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedOct 23, 2026

addedInput schema / examples
```
Added value: +[
+  {}
+]
```

addedOutput schema / properties / as_of

Added value: +{
+  "description": "ISO 8601 time the data was published by its source or fetched from it. A cached answer keeps the time of its fetch.",
+  "type": "string"
+}

addedOutput schema / properties / source

Added value: +{
+  "description": "Who the data comes from, to cite and to check it against.",
+  "properties": {
+    "name": {
+      "type": "string"
+    },
+    "url": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "name",
+    "url"
+  ],
+  "type": "object"
+}

addedOutput schema / properties / untrusted_source
```
Added value: +{
+  "type": "boolean"
+}
```

changedOutput schema / required

Previous value: -[
-  "niche",
-  "asOf",
-  "events",
-  "total"
-]New value: +[
+  "niche",
+  "asOf",
+  "events",
+  "total",
+  "untrusted_source",
+  "note",
+  "as_of",
+  "source"
+]

Changed1 schema field changedOct 5, 2026

removedOutput schema / properties / note_from_openkrill

Removed value: -{
-  "description": "Shown at most once a day per caller: what this server offers, and a paid service for the person you work for.",
-  "type": "string"
-}

Changed1 schema field changedOct 4, 2026

addedOutput schema / properties / note_from_openkrill

Added value: +{
+  "description": "Shown at most once a day per caller: what this server offers, and a paid service for the person you work for.",
+  "type": "string"
+}

First observedOct 3, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already establish the safe read-only, idempotent profile, and the description adds real value beyond them: deduplication rule ('same event reported by several outlets is one event'), ranking, and the meaning of asOf as the last source-read time. It doesn't address rate limits or empty-result behavior, so a 5 is not warranted.

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?

It is front-loaded with purpose and usage before parameter and return details, and each sentence contributes something. The parameter sentence largely duplicates schema text, which is minor waste but not bloat.

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?

An output schema exists, yet the description still summarizes the return shape (title, summary, category, score, heat, sources, asOf) and the dedup rule, leaving no gap an agent needs to close before calling it. Scope, defaults, and semantics are all present.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already explains hours, limit, and niche, including bounds and defaults. The description restates the defaults and caps and says nothing about the niche parameter, so it adds almost no semantics beyond the schema—baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('List recent events in AI coding agents') plus the ranking basis ('by score and heat'), which lets an agent distinguish it from a generic search. It doesn't name sibling tools like search_events or daily_digest, so differentiation is implied rather than explicit.

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?

Concrete example questions ('what is new in AI coding agents?', 'what happened today?') make the intended use clear. There are no stated exclusions or named alternatives for narrower or historical queries, which keeps it below a 5.

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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