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Glama

Ask about HubSpot companies

ask_about_hubspot_companies
Read-only

Query the user's HubSpot companies — the companies synced from the HubSpot portals they've connected. Use this for any question about their CRM companies: firmographics ('software companies with more than 100 employees'), lifecycle and pipeline ('companies with an open deal', 'how many customer accounts?'), location ('companies in Boston'), funding and size ('which companies raised money?', 'biggest companies by revenue'), or attribution. Answered by generating a read-only SQL query over the synced company table, so it returns columns and rows rather than prose — summarize the rows for the user, and say how many there were. If it reports no companies synced, tell them to run a company sync for their portal. Keep the question under 500 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesThe question about their HubSpot companies, in plain English. Pass the user's own phrasing where you can.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The annotations declare readOnlyHint=true, and the description adds meaningful behavioral context: it generates a read-only SQL query, returns columns/rows instead of prose, instructs summarizing rows with a count, and advises triggering a sync if no companies are found. This goes well beyond the structured annotation.

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 well-organized: clear purpose first, then examples, then behavioral guidance, then a practical edge case. Every sentence contributes useful information without redundancy or fluff.

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 tool has only one parameter and no output schema, the description covers all necessary operational details: what it queries, how results are returned, how to present them, and what to do when no data is synced. Nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents the 'question' parameter fully, so the baseline is 3. The description adds extra value by specifying the 500-character limit and instructing to pass the user's own phrasing, which helps the agent invoke the tool correctly.

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 uses a specific verb ('Query') and clearly identifies the resource ('the user's HubSpot companies'). It explicitly distinguishes this tool from the sibling ask_about_hubspot_contacts by focusing on synced company data and gives concrete examples of supported question types.

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 explicitly says 'Use this for any question about their CRM companies' and lists question categories, which provides a clear usage context. However, it does not name any alternative tools or state when not to use this tool, so the guidance is clear but lacks explicit exclusions.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, with clear boundaries even within overlapping domains like LinkedIn (search vs. free-form query vs. profile vs. summary) and graph deletion (soft single, bulk soft, permanent single). Descriptions explicitly cross-reference related tools to prevent misselection.

Naming Consistency4/5

The vast majority follow a consistent verb_noun pattern (get_, list_, search_, create_, delete_, etc.). A few noun-phrase exceptions like linkedin_analytics, mutual_connections, similar_objects, and what_needs_attention deviate slightly, but they are still descriptive and do not create confusion.

Tool Count2/5

At 66 tools this is far beyond the 25+ threshold considered too many, even though the server covers many integration domains. Each domain has a coherent subset, but the overall surface is heavy for agents to navigate and would benefit from consolidation or namespacing.

Completeness4/5

The set provides deep read/search coverage across Gmail, Slack, Calendar, LinkedIn, HubSpot, Obsidian, Twitter, and a graph store, with core write operations for calendar, drafts, Slack, and graph objects. Minor gaps exist—notably no calendar delete, no direct Gmail send to third parties (only drafts), and no LinkedIn post/message actions—but these appear deliberate and do not block typical workflows.