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Glama

Statbel Be

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

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds substantial behavioral context: parallel fan-out across sources, soft-fail behavior for the USPTO sunset, 'sources_used/sources_failed' semantics, and the expected 'no data, not a bug' case for FDA products or empty sections. No contradiction with annotations.

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 a single dense paragraph, but nearly every clause carries valuable semantic or behavioral information. It is front-loaded with triggering phrases and the core purpose before enumerating sources and edge cases. Minor structure improvements could split it into sections, but it is not bloated with filler.

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?

With no output schema, the description carries the full burden of explaining return sections, source-level failure semantics, input resolution, and edge cases. It explicitly covers cik/company_name, recent_filings URIs, fundamentals sorting, patents sunset, federal contracts, FDA products, hiring, news fallback, LEI, and source status fields. An agent has enough context to call this correctly and interpret results.

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 both parameters are already documented. The description adds meaning by explaining that `type` accepts 'company' or 'ticker' interchangeably with the same `value` shapes, that names resolve via SEC EDGAR, and that a private company returns resolved:false with a notes line. This goes beyond the raw schema without being redundant.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as a 'full cross-source profile of a US public company in ONE parallel call' with explicit trigger examples like 'Tell me about X' and 'research Acme.' It distinguishes itself from 'chaining single-pack SEC/XBRL/news lookups,' though it does not explicitly name profile-adjacent siblings such as compare_entities or resolve_entity.

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 provides explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies behavior for private companies and accepted input forms. However, it does not state when NOT to use this tool or how it relates to comparison/resolution sibling tools.

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

A3.7/5.0
Disambiguation2/5

The set contains several clusters of overlapping tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve broad data-query purposes, while the six Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap in scope. The detailed descriptions help, but an agent would frequently struggle to choose the correct tool among near-synonyms.

Naming Consistency3/5

All names use snake_case, but the underlying pattern is inconsistent: list_*/get_* for Statbel, ask_* for query routers, noun-heavy names like entity_profile, bet_research, polymarket_edges, and recent_alerts, plus bare verbs like remember, recall, forget, subscribe, unsubscribe. It remains readable, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

At 35 tools, the set exceeds the range where each tool clearly earns its place, and the scope is wildly broad: Belgian statistics, general data lookup, prediction markets, npm dependency checks, memory, subscriptions, llms.txt generation, and AI visibility. A server named 'Statbel Be' carries 31 tools unrelated to that name, which makes the count feel bloated and unfocused.

Completeness2/5

For the Statbel domain implied by the server name, the surface is severely incomplete: list_datasets, get_dataset, list_views, and get_view only return metadata — there is no tool to actually fetch the statistical data values. For the broader Pipeworx data-access domain, coverage is more complete, but the server's stated purpose is under-served and leaves core workflows at a dead end.

Resources