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Glama

noticed

by so.noticed.mcp

list_intros

Read-onlyIdempotent

The user's pending introductions — who offered, who they'll be connected to, and where each stands (waiting/ready/requested/done/dropped). Answers "what intros am I waiting on?" and "who promised to connect me to someone?".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (default 50).
statusNoFilter by lifecycle status.
connector_person_idNoOnly intros offered by this person.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether noticed completed the operation.
dataNoThe operation result when ok is true.
errorNoA human-readable error when ok is false.

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: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "description": "The operation result when ok is true.",
+      "properties": {
+        "intros": {
+          "items": {
+            "additionalProperties": true,
+            "properties": {
+              "connector_name": {
+                "type": "string"
+              },
+              "connector_person_id": {
+                "type": "string"
+              },
+              "id": {
+                "type": "string"
+              },
+              "notes": {
+                "$ref": "#/properties/data/properties/intros/items/properties/target_org"
+              },
+              "resolved_person_id": {
+                "$ref": "#/properties/data/properties/intros/items/properties/target_org"
+              },
+              "status": {
+                "type": "string"
+              },
+              "target_name": {
+                "type": "string"
+              },
+              "target_org": {
+                "type": [
+                  "string",
+                  "null"
+                ]
+              }
+            },
+            "type": "object"
+          },
+          "type": "array"
+        },
+        "total": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "intros",
+        "total"
+      ],
+      "type": "object"
+    },
+    "error": {
+      "description": "A human-readable error when ok is false.",
+      "type": "string"
+    },
+    "ok": {
+      "description": "Whether noticed completed the operation.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "ok"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds the lifecycle statuses and the nature of the list (offered, connected-to), which goes beyond the schema's minimal descriptions. No contradiction with 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?

A single, efficient sentence that front-loads the primary function and includes the useful state enumeration. No fluff, every word contributes.

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 the output schema exists and annotations cover safety, the description fully covers the essential semantic context: what the list contains, the statuses, and the user questions it resolves. Nothing critical is missing.

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% with clear per-parameter descriptions. The tool description repeats the status values already in the enum but adds no semantic depth beyond what the schema supplies. Baseline of 3 is appropriate given high schema coverage.

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?

The description clearly identifies the tool as listing pending introductions, specifying the key data elements (who offered, target person, status) and explicitly states the user questions it answers. This is specific and distinct from the sibling tools like track_intro.

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?

It frames usage around answering 'what intros am I waiting on?' and 'who promised to connect me to someone?', giving clear context for when to invoke it. It doesn't explicitly mention alternatives or exclusions, but the context is strong enough to guide an agent.

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

B3.4/5.0
Disambiguation3/5

The tool set is organized around distinct resources, and the descriptions work hard to separate them, but several close pairs remain easy to confuse: add_memory vs memory_save vs add_note, accept_identity_match vs suggest_identity_match, and dismiss_identity_match vs mark_different_people. An agent will often need to read very subtle signals (who originated the content, pending vs initiating a merge, soft vs durable rejection) to pick the right tool.

Naming Consistency3/5

Most tools follow a clear verb_noun snake_case pattern like create_list, update_person, and delete_view, which is readable and mostly predictable. However, the memory tools break the pattern (memory_save, memory_get, memory_search instead of save_memory/get_memory/search_memory), and a few noun-style names (my_profile, network_summary, account_status) add inconsistency.

Tool Count1/5

At 57 tools, this is an extremely large surface that exceeds the calibration threshold for an extreme mismatch. The scope is broad, but many tools are micro-specialized variations of the same concept, such as four memory-related tools and seven identity-match tools, which makes the count feel inflated rather than well-scoped.

Completeness4/5

The tool set provides thorough lifecycle coverage for the core domain: people can be added, updated, searched, and removed; lists, views, actions, and scheduled tasks have create/read/update/delete; and identity matching has accept, dismiss, differentiate, and suggest paths. Minor gaps exist, such as no direct memory/note deletion or intro deletion, but agents can generally complete workflows without hitting dead ends.

Resources