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Glama

Passwordgen

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?

Discloses far beyond the readOnly/openWorld/idempotent annotations: the parallel fan-out across nine named sources, the specific return fields (recent_filings up to 5 with pipeworx URIs, LATEST 10-K fundamentals), and failure semantics ('sources_failed', 'an empty section is a real no data, not a bug'). It also preempts confusion on expected absences — a small-molecule-only drug company showing no fda_products 'is expected, not a failure' — and describes the private-company resolved:false case. All of this is consistent 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.

Conciseness3/5

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

The description is information-dense and every clause carries real content, but it is a roughly 280-word single block with deep parenthetical chains that are difficult to scan. It also contains a visible garbled artifact in the fundamentals section ('Cash, sorted Cash, sorted...') that undermines trust in the return-value enumeration. Front-loading of example queries and the ALWAYS PREFER directive is good, but the wall-of-text structure and the corruption prevent a higher score.

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 present, the description carries the full burden of documenting the return shape, and it does so exhaustively: every section (cik, company_name, resolved_from/to, recent_filings with URI format, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) is named with its source and semantics. Input variants, resolution behavior, expected absences, and sources_used/sources_failed semantics are all covered. The only notable gap is the garbled fundamentals detail.

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% and the schema already provides examples, the SEC EDGAR resolution note, and the type-interchangeability statement, setting a baseline of 3. The description adds value beyond the schema by specifying private-company behavior (resolved:false plus an explicit notes line), emphasizing zero-padding for CIKs, and confirming that type accepts 'company' or 'ticker' interchangeably for all value shapes.

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 the exact task — "full cross-source profile of a US public company in ONE parallel call" — backed by a rich set of natural-language triggers ('tell me about X', 'research Acme', 'brief me on Tesla', 'company profile for Microsoft'). The scope is precise (US public companies only, single entity) and the explicit 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' directive carves out its niche against siblings like deep_research, compare_entities, and 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 gives an explicit trigger and preference rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' plus a clear out-of-scope behavior for private companies (resolved:false with a notes line). However, it does not route adjacent intents to any sibling — e.g., deep_research for deeper dives or compare_entities for cross-company comparisons — so exclusions are partial rather than complete.

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

A3.8/5.0
Disambiguation4/5

Tools have distinct purposes described in detail; however, several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) overlap in general domain, causing minor ambiguity.

Naming Consistency2/5

No consistent naming pattern: mix of verb_noun (ask_pipeworx), noun_verb (bet_research), descriptive (entity_profile), and inconsistent use of underscores/camelCase.

Tool Count2/5

32 tools is excessive for a single server, especially when the server name 'Passwordgen' only covers 2 tools; the rest form a sprawling data platform that could be split.

Completeness3/5

As a password generator it is missing features like strength checking; as a data platform it covers queries and subscriptions but lacks write capabilities, leaving notable gaps.

Resources