get_entityFull record for one entity by 9-digit org number. e.g. {orgnr:"923609016"} (Equinor ASA). Returns navn, organisasjonsform, naeringskode1, forretningsadresse, antallAnsatte, stiftelsesdato, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| orgnr | Yes | 9-digit org number, e.g. "923609016". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "orgnr": "923609016"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering the safety profile. The description adds value by listing the returned fields (navn, organisasjonsform, etc.) and providing an example, which is not present in the input schema. This clarifies what the agent can expect in the response.
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 description is two sentences, front-loaded with the core purpose, and uses a compact example. No filler or redundant phrasing.
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 simple one-parameter read-only lookup, the description covers the purpose, parameter format, and sample output fields. Without an output schema, it lists key return fields, which is sufficient for agent invocation. A minor gap is lack of explicit mention of error cases or prerequisites, but these are not critical for such a straightforward tool.
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 single parameter orgnr is fully described in the schema (100% coverage) with the same 9-digit format and example. The description's repetition adds no new syntactic details, though the Equinor ASA example gives a concrete real-world instance.
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 clearly states the tool returns the full record for one entity identified by a 9-digit org number, with a concrete example (Equinor ASA). This distinguishes it from sibling tools like search_entities or compare_entities.
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 implies the intended use case (lookup by org number) but does not explicitly contrast with alternatives such as search_entities or get_sub_entity. There is no when-to-use vs when-not-to-use guidance beyond the narrow focus on a single org number.
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.
ask_pipeworx_beta is explicitly identical to ask_pipeworx, and multiple other tools overlap heavily: ai_visibility_check/scan_competitor_ai_presence, discover_tools/suggest_questions, and polymarket_arbitrage/polymarket_edges/polymarket_edge_tracker all sit in nearly the same functional space. An agent would struggle to reliably select the right tool among these clusters.
Tool names are consistently snake_case and mostly readable, but the pattern is mixed: get_/search_ verbs coexist with product-prefixed names (pipeworx_*, polymarket_*), noun-style names (entity_profile, recent_changes), and bare verbs (remember, forget). The conventions are not chaotic, but they are not predictable enough for a coherent set.
37 tools is far too many for a server named 'Brreg No,' which should be a focused Norwegian business-registry lookup server. Only a handful of tools actually target Brreg (search_entities, get_entity, get_accounts, get_roles, get_sub_entity, search_sub_entities); the rest are unrelated Pipeworx/Polymarket/meta tools that drown out the core purpose.
The core Brreg read surface is present: entity search/lookup, sub-entities, financial accounts, and roles. However, there is no Brreg change/update feed or document-level coverage, and the unrelated generic research tools do not fill that gap. For a registry-focused server, the coverage is workable but not complete.