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Glama

Kroger

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

A4.9/5.0
Behavior5/5

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

The description goes well beyond the readOnly/idempotent annotations by disclosing soft-fail behavior for patents, expected empty sections like fda_products for small-molecule companies, the sources_used/sources_failed contract, and the resolved:false path for private companies. This gives an agent realistic expectations about data availability and failure modes without contradicting any 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 dense and front-loaded with example queries and the core 'prefer' directive. It is longer than necessary because it repeats some parameter semantics already present in the schema and includes an extensive but useful list of sources; overall it remains well-organized and mostly efficient.

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 does a thorough job of explaining return sections such as cik, company_name, recent_filings URI patterns, and fundamentals. It also defines the sources_used/sources_failed contract and input resolution edge cases, which is essential for correctly interpreting results from such a multi-source tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema already documents both parameters well, the description adds crucial semantic detail: name resolution via SEC EDGAR, zero-padded CIK format, accepted ticker/CIK/name forms, and the private-company resolved:false outcome. It also clarifies that 'type' values are interchangeable, reinforcing correct invocation.

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 clearly identifies the tool as producing a full cross-source profile of a US public company in one parallel call, supported by example user phrasings. It explicitly distinguishes itself from single-pack SEC/XBRL/news lookups, so an agent can tell what kind of holistic request this tool is meant for.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit decision rule: always prefer this over chaining single-pack lookups when the user asks for a holistic view. This is a strong when-to-use signal and names the main alternative behavior, making selection straightforward.

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

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research all query data), making it hard for an agent to choose the correct one. Additionally, entity_profile and compare_entities both provide company profiles, adding further ambiguity.

Naming Consistency2/5

Tool names are inconsistent: some use verb_noun (ask_pipeworx, compare_entities), some noun_verb (entity_profile), and some are single verbs (forget, recall). There is no clear pattern, and the mix of styles (e.g., kroger_*, polymarket_*, pipeworx_*) adds confusion.

Tool Count3/5

34 tools is on the higher end, but the server covers a broad domain (grocery, data query, betting, subscriptions). However, the presence of multiple similar query tools (e.g., three ask_pipeworx variants) inflates the count unnecessarily, making it feel bloated.

Completeness3/5

For its stated Kroger grocery focus, the tools (search, details, store locator) are complete. However, the server also includes many unrelated tools (betting, entity profiles, subscriptions) that feel tacked on, leaving gaps in coverage for the non-Kroger domains (e.g., no tools for user management or other store chains).

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