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Glama

Brreg No

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent/openWorld annotations by disclosing source fan-out, returned fields, soft-failure of USPTO patents, GDELT→GNews fallback, empty-section semantics, source_used/source_failed behavior, and private-company resolved:false handling. Agent can predict failure modes without invoking.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

Front-loaded with examples and the core purpose, and nearly every sentence carries operational information. However, the description is a single dense run-on paragraph with long parenthetical enumerations, making it harder to scan than it should be for an agent.

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 complex multi-source tool with no output schema, the description is remarkably complete: it names all data sources, defines the returned sections, explains expected empty results, documents input variants, and specifies private-company failure behavior. 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?

Input schema already documents both parameters at 100% coverage. Description adds real value by giving concrete examples ('AAPL', '0000320193', 'Moderna'), explaining that type is interchangeable, and clarifying that a company name resolves via SEC EDGAR match. This goes beyond schema paraphrase.

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?

Description opens with concrete user phrasings and then states 'full cross-source profile of a US public company in ONE parallel call' — a specific verb, resource, and scope. It also distinguishes itself from single-pack lookups by name and from siblings like resolve_entity/get_entity through the 'holistic view' framing.

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?

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which gives clear when-to-use guidance. It does not enumerate exclusions for closely related siblings like compare_entities or deep_research, so it misses full alternative differentiation.

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
Disambiguation1/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness3/5

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.

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