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Glama

Ai Briefing

by io.github.pipeworx-io

What Happened

what_happened
Read-onlyIdempotent

Ask natural language questions about recent tools and developments (e.g., 'any new MCP servers this week', 'latest Claude tools'). Returns the most relevant developments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLook back N days (default 30)
questionYesYour question about recent AI developments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesNumber of matching developments found
periodYesTime period description
questionYesOriginal natural language question
developmentsYesMost relevant developments
keywords_usedYesExtracted keywords from question

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "developments": {
+      "description": "Most relevant developments",
+      "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"
+    },
+    "keywords_used": {
+      "description": "Extracted keywords from question",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "period": {
+      "description": "Time period description",
+      "type": "string"
+    },
+    "question": {
+      "description": "Original natural language question",
+      "type": "string"
+    },
+    "total": {
+      "description": "Number of matching developments found",
+      "type": "number"
+    }
+  },
+  "required": [
+    "question",
+    "keywords_used",
+    "period",
+    "total",
+    "developments"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "question": "any new MCP servers this week"
+  },
+  {
+    "days": 14,
+    "question": "latest Claude tools"
+  }
+]
  3. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds the behavioral detail 'Returns the most relevant developments', which is helpful but minimal. No additional traits (e.g., pagination, freshness) are disclosed. Since annotations are rich, the description's contribution is modest but not contradictory.

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?

The description is a single sentence followed by two examples, front-loading the core action. Every sentence is necessary, and there is no filler. It is appropriately concise for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters, rich annotations, and an output schema, the description covers the essential context: what the tool does, how to use it (via examples), and what it returns. It is complete for a straightforward NLQ tool, though additional context about result sorting or typical use cases could elevate it.

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?

The input schema already documents both parameters with descriptions (100% coverage). The description provides example values that illustrate natural language usage, adding some contextual meaning beyond the schema. However, it does not explain parameter semantics beyond what is in the schema.

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 the verb 'ask' and the resource 'recent tools and developments', with concrete examples. This clearly defines the tool's purpose. However, it does not explicitly differentiate from similar siblings like 'search_developments' or 'get_ai_news', missing a chance to clarify distinction.

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

Usage Guidelines3/5

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

The description implies usage via examples ('any new MCP servers this week') but provides no explicit guidance on when to use this tool versus alternatives, nor when not to use it. Given many sibling tools, a note on scope or preferred contexts would improve decision-making.

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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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