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Glama

Hubspot

by io.github.pipeworx-io

Hs Get Company

hs_get_company
Read-onlyIdempotent

Fetch a company's full profile by ID. Returns name, domain, industry, revenue, and all custom properties.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesHubSpot company ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoCompany ID
archivedNoWhether company is archived
createdAtNoCreation timestamp
updatedAtNoLast update timestamp
propertiesNoCompany properties (name, domain, industry, revenue, custom fields)

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": {
+    "archived": {
+      "description": "Whether company is archived",
+      "type": "boolean"
+    },
+    "createdAt": {
+      "description": "Creation timestamp",
+      "type": "string"
+    },
+    "id": {
+      "description": "Company ID",
+      "type": "string"
+    },
+    "properties": {
+      "description": "Company properties (name, domain, industry, revenue, custom fields)",
+      "type": "object"
+    },
+    "updatedAt": {
+      "description": "Last update timestamp",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "id": "67890"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description carries a lighter burden. It adds context by listing returned fields, which is helpful but does not disclose additional behavioral traits beyond what annotations imply.

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?

Two sentences, front-loaded with the action and resource, no unnecessary words. Every sentence adds value.

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?

With only one parameter, full schema coverage, annotations present, and an output schema (known from context), the description is complete. It adequately describes what the tool does and returns.

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% with a clear description for the only parameter 'id'. The description adds 'by ID' but no further meaning beyond the schema. Baseline 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 action ('Fetch'), the resource ('company's full profile by ID'), and enumerates return fields (name, domain, industry, revenue, custom properties). This distinguishes it from sibling tools like hs_get_contact or hs_get_deal.

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 when you have a company ID, but provides no explicit guidance on when to use this tool versus alternatives like hs_list_companies or hs_search_contacts. No exclusions or context are mentioned.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates or overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve overlapping query/discovery purposes. The polymarket tools and HubSpot tools are more distinct, but the set as a whole has fuzzy boundaries between many members.

Naming Consistency2/5

Naming is inconsistent across the set: HubSpot tools use an hs_ prefix, Pipeworx tools mostly use bare verbs (ask_pipeworx, recall, forget), and other tools mix styles (ai_visibility_check, generate_llms_txt, polymarket_edges). The hs_* subset is consistent, but overall there is no single predictable pattern.

Tool Count2/5

38 tools is too many for the apparent scope, especially since the server is named 'Hubspot' but only 6 of the tools are HubSpot-specific. A large portion of the catalog covers unrelated Pipeworx data access, prediction markets, memory, and npm scanning, making the set feel bloated and unfocused.

Completeness2/5

The HubSpot portion of the tool set is read-only: it can get, list, and search companies/contacts/deals, but has no create, update, or delete operations, leaving obvious lifecycle gaps. If the intended domain is actually Pipeworx/data research, the HubSpot tools seem like an unrelated afterthought, so the surface is incomplete for either interpretation.

Resources