get_company_factsVerified company facts: legal name, founding, headquarters, contacts and office hours, factory and capacity narrative, lead-time and warranty commitments, certifications, standards position, quote channel and key URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the tool is known to be a safe read. The description adds meaningful content context ('verified company facts' plus categories), but does not disclose behavioral details such as whether the data is static, whether all facts are returned in one call, or how the response is structured. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is a single, dense sentence that front-loads the key phrase 'Verified company facts' and then delivers a comma-separated enumeration of all content categories. Every item earns its place; no filler words or redundant explanations appear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, no-output-schema tool, the description gives a thorough inventory of what the agent can expect to find, enabling correct selection for questions about company facts. The only notable gap is lack of clarification on the boundary with get_lead_time, but overall the tool is well-specified for its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema description coverage is 100%, so the description has no parameter burden. The description helpfully lists the output content areas, which substitutes for absent output schema detail. Baseline for zero-parameter tools is 4, and no deduction is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb and resource ('Verified company facts') and enumerates a specific scope: legal name, founding, headquarters, contacts, office hours, factory/capacity narrative, lead-time/warranty commitments, certifications, standards, quote channel, and URLs. This makes its purpose distinct from the sibling tools, which target products, lead times, and quote handoff.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description strongly implies use when a user needs company-level facts, by listing the exact categories it covers. However, it does not explicitly state when to use this tool vs. alternatives such as get_lead_time, which may overlap with the 'lead-time commitments' item. No direct routing guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target clearly distinct resources and actions, such as products, company facts, lead times, RFQ handoff, semantic search, and EV capacity estimation. The only mild overlap is get_lead_time versus the lead-time band already embedded in get_product, and search_knowledge can return passages that overlap with structured tools.
Every tool follows a consistent verb_noun snake_case pattern: estimate_, get_, list_, request_, and search_. There are no mixed naming conventions, generic verbs, or vague tool names.
Seven tools is a well-scoped count for a knowledge-and-estimation server. Each tool covers a distinct need without filler or redundant entries.
The tool set supports product discovery, detailed product lookup, company facts, lead times, quote handoff, semantic search, and EV charging estimation, covering core user journeys. A minor gap is that other configurators mentioned in the quote handoff, such as the transformer configurator, are only exposed as links rather than executable tools.