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Glama

Intercom

by io.github.pipeworx-io

Ic Get Contact

ic_get_contact
Read-onlyIdempotent

Get full contact details by ID. Returns name, email, phone, attributes, tags, and conversation history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesContact ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoContact ID
nameNoContact name
tagsNo
emailNoContact email address
errorNoError code if connection required
phoneNoContact phone number
messageNoError message if connection required
conversationsNoConversation history
custom_attributesNoCustom contact attributes

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": {
+    "conversations": {
+      "description": "Conversation history",
+      "type": "object"
+    },
+    "custom_attributes": {
+      "description": "Custom contact attributes",
+      "type": "object"
+    },
+    "email": {
+      "description": "Contact email address",
+      "type": "string"
+    },
+    "error": {
+      "description": "Error code if connection required",
+      "type": "string"
+    },
+    "id": {
+      "description": "Contact ID",
+      "type": "string"
+    },
+    "message": {
+      "description": "Error message if connection required",
+      "type": "string"
+    },
+    "name": {
+      "description": "Contact name",
+      "type": "string"
+    },
+    "phone": {
+      "description": "Contact phone number",
+      "type": "string"
+    },
+    "tags": {
+      "properties": {
+        "data": {
+          "items": {
+            "properties": {
+              "id": {
+                "type": "string"
+              },
+              "name": {
+                "type": "string"
+              }
+            },
+            "type": "object"
+          },
+          "type": "array"
+        }
+      },
+      "type": "object"
+    }
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "id": "507f1f77bcf86cd799439011"
+  }
+]
  3. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by listing the specific data returned (including conversation history), which annotations do not cover. No contradictions.

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 with a bullet-style list of return fields. It is front-loaded with the core purpose and all information is valuable and non-redundant.

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 simple one-parameter input, the presence of an output schema (which likely details return structure), and comprehensive annotations, the description sufficiently covers what the tool does and returns. No important 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% and the single parameter 'id' is described with a brief explanation ('Contact ID') in the schema. The description adds no further semantic detail about the parameter, so baseline score of 3 is appropriate.

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 states the tool gets full contact details by ID and lists specific return fields (name, email, phone, attributes, tags, conversation history). This distinguishes it from siblings like ic_search_contacts (search) and ic_get_conversation (conversation-specific).

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives such as ic_search_contacts or ic_list_companies. While the purpose is clear, the lack of explicit when-to-use or when-not-to-use advice reduces usability for an agent deciding between tools.

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

All Intercom toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route factual questions to the same underlying catalog with unclear boundaries. Polymarket tools also overlap (bet_research, polymarket_edges, polymarket_arbitrage), and ai_visibility_check vs scan_competitor_ai_presence are nearly the same operation.

Naming Consistency3/5

Most tools follow a readable snake_case verb_noun pattern (ask_pipeworx, entity_profile, resolve_entity, list_subscriptions). However, the set mixes two distinct naming families — ic_* for Intercom tools and pipeworx/* for the rest — and includes ambiguous generic names like remember/recall/forget that don't visually connect to the rest.

Tool Count2/5

36 tools is far more than needed for an Intercom-focused server; the vast majority have nothing to do with Intercom and instead cover a sprawling Pipeworx data-research, prediction-market, and memory/subscription toolkit. The count alone is in the 'too many' range, and the scope mismatch makes it feel even more inflated.

Completeness2/5

For a server named Intercom, the surface is severely incomplete: only read/list/search operations exist for contacts and conversations, with no create, update, delete, send-message, or company-detail operations. The unrelated Pipeworx tools are fairly broad, but they don't compensate for the missing Intercom lifecycle coverage that the server name promises.

Resources