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Glama

Ripe Stat

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?

Annotations already mark the tool read-only, idempotent, and non-destructive, and the description adds rich behavioral detail: cross-source fan-out, USPTO patent soft-failure, FDA empty-section expectations, sources_used/sources_failed, and resolved_from/resolved_to behavior. There is no contradiction with the 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 long but dense, with trigger examples and the 'ALWAYS PREFER' guidance front-loaded before source/output details. Some sentences repeat schema content, but the length is justified for a multi-source tool with this many behavior notes.

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 compensates thoroughly by enumerating return sections, source lists, expected empty results, soft-failure behavior, and private-company resolution. It gives an agent enough context to invoke the tool correctly and interpret results plausibly.

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

Parameters3/5

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

Schema description coverage is 100% and the schema already documents type/value semantics, including ticker/CIK/name resolution and the interchangeability of 'company' and 'ticker'. The description mostly restates these input facts, adding output behavior like resolved:false rather than new parameter meaning, so the baseline 3 applies.

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 anchors the tool with natural-language triggers ('Tell me about X', 'research Acme', 'brief me on Tesla') and an explicit definition: 'full cross-source profile of a US public company in ONE parallel call.' It names the exact resource and scope, and it is clearly distinguishable from the single-pack SEC/XBRL/news lookups it is meant to replace.

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 explicitly instructs agents to 'ALWAYS PREFER' this tool over chaining single-pack lookups when the user asks for a holistic view. It also covers edge cases like private companies returning resolved:false, so the agent knows when and how the tool applies.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially in networking and domain-specific queries. However, the multiple `ask_pipeworx` variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and `deep_research` have overlapping functionality, which could cause occasional misselection.

Naming Consistency2/5

Tool names use inconsistent conventions: some are concatenated (geoloc, whois), some use underscores (abuse_contact, as_overview), and some are multi-word (scan_competitor_ai_presence, ask_pipeworx_grounded). No consistent pattern across the set.

Tool Count3/5

39 tools is high for a single server, but justified by the broad scope covering networking, finance, prediction markets, AI visibility, and more. However, many tools are meta-tools for the Pipeworx ecosystem, making the set feel slightly bloated.

Completeness5/5

The server covers an exceptionally wide range of domains comprehensively, with tools for networking, company data, FDA datasets, prediction markets, weather, news, and more. It includes memory and subscription management, leaving few obvious gaps for its stated data-retrieval purpose.

Resources