Skip to main content
Glama

Cbs Nl

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

A4.6/5.0
Behavior5/5

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

The description goes far beyond the readOnly/idempotent annotations, disclosing fan-out sources, source-level failure semantics, expected empty sections for fda_products, soft-fail behavior for patents, and the resolved:false path for private companies. It also explicitly explains that an empty section means 'no data', not a bug.

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 long, but the length is justified by the number of data sources and edge cases it covers. It is front-loaded with example user requests and the key 'one parallel call' value proposition, though it is written as a dense single block rather than structured sections.

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?

Given the tool's complexity and the absence of an output schema, the description is remarkably complete. It covers accepted inputs, all major returned sections, source fallback behavior, expected empty results, and the private-company failure mode, leaving little for an agent to guess.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds useful meaning: type values are interchangeable, value can be a ticker, zero-padded CIK, or company name, and names resolve via SEC EDGAR company-name matching. It also covers the private-company response behavior, which is helpful beyond the schema.

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 states an exact purpose: produce a full cross-source profile of a US public company in one parallel call. It opens with concrete user intents like 'tell me about X' and 'research Acme', and it clearly distinguishes this holistic tool from single-pack SEC/XBRL/news lookups.

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?

It explicitly says to prefer this tool over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. However, it does not directly contrast with sibling tools like compare_entities, resolve_entity, or deep_research, so some ambiguity about the boundaries with those alternatives remains.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Cbs Nl toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions with subtle differences that are hard to distinguish (beta is currently identical to the stable version). Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_fill_risk) similarly overlap in opportunity-finding. Entity_profile, compare_entities, and recent_changes also share company-research territory.

Naming Consistency3/5

All tool names use snake_case, which is consistent, but the structural pattern varies widely: some are verb_noun (get_data, search_tables), some are noun_phrase (table_dimensions, entity_profile), some are brand-prefixed (pipeworx_trending, polymarket_edges), and the memory tools (remember, recall, forget) break the pattern entirely. Mixed conventions make the set feel less coherent.

Tool Count2/5

With 36 tools, the set is heavy, and the server name 'Cbs Nl' implies a focused CBS statistics dataset, yet most tools cover unrelated domains (Polymarket, AI visibility, npm dependencies). Even as a general data-research platform, the count exceeds the 25-tool threshold for 'heavy', and many tools could be consolidated (e.g., the three ask_pipeworx variants).

Completeness4/5

As a general data-research platform, the tool surface is fairly complete: discovery (discover_tools, search_tables, suggest_questions), metadata (table_info, table_dimensions), retrieval (get_data, ask_pipeworx, deep_research), validation (validate_claim, compare_entities), and supporting features (memory, subscriptions, feedback). Minor gaps include no explicit tool to manipulate data or manage sources, but for a read-heavy research assistant, the coverage is strong.

Resources