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Glama

Hubspot

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

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

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds details about parallel calls, fallback behavior (GDELT→GNews), soft-fail for USPTO, and rate-limiting context. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficient and front-loaded with example queries, but is a single dense paragraph. Slight improvement possible with structure, but overall concise and informative.

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?

Despite no output schema, the description explains return format (grouped changes, total_changes, citation URIs). Also notes limitation (only 'company' type). Complete for a tool with 3 parameters.

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?

All parameters have schema descriptions (100% coverage), but the description adds value by explaining `since` with relative shorthand examples and clarifying `value` accepts tickers or CIK. Justifies a 4 rather than baseline 3.

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's purpose as a change feed for a company, listing example queries and specific sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from sibling 'entity_profile' by noting the difference between dynamic changes and static profile.

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

Usage Guidelines5/5

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

Explicitly states when to use (e.g., 'What's new' queries) and provides an alternative: 'Use entity_profile instead when you want the static profile'. This gives clear guidance on tool selection.

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.

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