Skip to main content
Glama

Ai Briefing

by io.github.pipeworx-io

Get Briefing

get_briefing
Read-onlyIdempotent

Get the daily AI tools digest for a given date (default: today) — new MCP servers, APIs, SDKs, and frameworks released in the last 24 hours, with summaries and source URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate in YYYY-MM-DD format (default: today)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate in YYYY-MM-DD format
noteNoNote when live query used instead of cached digest
summaryNoDaily briefing summary
top_storiesNoTop stories from the day
developmentsNoDevelopments from live query
model_updatesNoModel release updates
paper_highlightsNoHighlighted research papers
total_developmentsNoTotal developments in live query

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "date": "2025-01-15"
-  },
-  {}
-]New value: +[
+  {},
+  {
+    "date": "2026-06-15"
+  }
+]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "date": {
+      "description": "Date in YYYY-MM-DD format",
+      "type": "string"
+    },
+    "developments": {
+      "description": "Developments from live query",
+      "items": {
+        "properties": {
+          "category": {
+            "description": "Development category",
+            "type": "string"
+          },
+          "importance": {
+            "description": "Importance level",
+            "type": "string"
+          },
+          "published_at": {
+            "description": "Publication timestamp",
+            "type": "string"
+          },
+          "source": {
+            "description": "Source identifier",
+            "type": "string"
+          },
+          "summary": {
+            "description": "Development summary",
+            "type": "string"
+          },
+          "title": {
+            "description": "Development title",
+            "type": "string"
+          },
+          "url": {
+            "description": "Source URL",
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "model_updates": {
+      "description": "Model release updates",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "note": {
+      "description": "Note when live query used instead of cached digest",
+      "type": "string"
+    },
+    "paper_highlights": {
+      "description": "Highlighted research papers",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "summary": {
+      "description": "Daily briefing summary",
+      "type": "string"
+    },
+    "top_stories": {
+      "description": "Top stories from the day",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_developments": {
+      "description": "Total developments in live query",
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "date": "2025-01-15"
+  },
+  {}
+]
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by specifying the type of content returned (new MCP servers, APIs, SDKs, frameworks with summaries and URLs), which informs the agent of the output structure beyond annotations.

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?

Single sentence front-loaded with purpose, no extraneous words. Efficiently conveys all necessary information.

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?

Given one optional parameter with full schema coverage, rich annotations, and an output schema, the description is complete. It explains what the tool returns and the parameter's role, leaving no gaps.

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 coverage is 100%, so baseline is 3. Description mentions the date parameter and its default, but this is already covered in the schema's description field. No additional semantic meaning added.

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?

Description clearly states verb 'Get', resource 'daily AI tools digest', and scope (given date, default today, last 24 hours). Distinguishes from siblings like get_ai_news or get_recent by specifying it's a curated digest of new MCP servers, APIs, SDKs, and frameworks.

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?

Clear context: use to get a daily curated list of new AI tools for a specific date. Does not provide explicit when-not-to-use or alternative sibling tools, but the context is sufficient for an agent to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Ai Briefing toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and numerous tools for AI news/tools (get_ai_news, get_ai_toolbelt, get_briefing, get_model_landscape, etc.). This will cause an agent to frequently misselect the appropriate tool.

Naming Consistency4/5

Most tools follow a verb_noun pattern in snake_case (e.g., compare_entities, discover_tools, get_briefing). However, a few deviate like 'bet_research' (noun_verb) and 'what_happened' (phrase), but overall the pattern is largely consistent.

Tool Count2/5

38 tools is excessive for a server called 'Ai Briefing', which suggests a focused purpose. The tool count spans multiple domains (AI visibility, Pipeworx queries, Polymarket betting, memory, subscriptions) making it feel overstuffed and unfocused.

Completeness3/5

The tool set covers many aspects of its broad domain (querying, comparing, subscribing, memory), but there are notable gaps: no tool for modifying subscriptions, no user profile management, and the AI news tools overlap rather than cover distinct needs.

Resources