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Glama

YouSpot

by com.youspot

Research company

research_company
Read-only

Research one specific company in depth — the same engine behind the CompanyResearch.ai app. Takes a website domain (preferred) or a company name, and returns a profile: description, industry, size, revenue, funding history, founders, competitors, recent news, and answers to the user's saved research questions. Use this when the user asks to research, analyze, or get a briefing on a specific company. For discovering many companies by criteria, use find_companies instead; for a quick identity card, company_card markup is enough. The first run on an uncached company can take a while — that is normal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYesThe company to research: a website domain ("hubspot.com", preferred) or a company name ("HubSpot").

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, and the description adds valuable behavioral context by warning that an uncached company can make the first run slow. It does not discuss errors or auth, but for a read-only research tool this is sufficient.

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 organized with purpose first, then input/output details, then alternatives, then the latency caveat. The 'same engine behind the CompanyResearch.ai app' clause adds only marginal value and could be trimmed, but the rest earns its place.

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?

For a one-parameter read-only tool with no output schema, the description is complete: it explains input format, return profile contents, usage context, alternatives, and the important latency behavior. Nothing essential for correct invocation 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?

The input schema already provides 100% coverage for the single `company` parameter, including the domain-vs-name guidance and examples. The description largely restates this rather than adding meaning beyond the schema, so the 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 opens with a specific verb and resource: 'Research one specific company in depth,' and enumerates the output profile fields. It also distinguishes itself from find_companies and company_card, making its purpose unmistakable.

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?

It explicitly states when to use the tool ('when the user asks to research, analyze, or get a briefing on a specific company') and names alternatives for related but different requests: find_companies for discovery and company_card for quick identity cards.

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.

Resources