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Glama

Google_calendar

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Goes far beyond the readOnly/idempotent annotations by disclosing fan-out across multiple data sources, fallback behavior (GDELT→GNews, patents soft-fail after May 2025), and the meaning of sources_used/sources_failed. It also explains that an empty section is 'a real no data, not a bug,' which is critical for agents interpreting results.

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 examples and core purpose. It is longer than strictly necessary, and some information (type/value shapes) is repeated in the schema, but every substantive behavior and edge case earns its place.

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?

For a complex tool with no output schema, the description fully documents return sections, source fallbacks, failure semantics, and input flexibility. It covers edge cases like private companies and expected empty results, so an agent has enough context to invoke it correctly and interpret the response.

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 covers the two parameters well, the description adds meaning: pass a ticker, zero-padded CIK, or company name; names resolve via SEC EDGAR; `type` accepts 'company' or 'ticker' interchangeably. It also details what the response will include (cik, company_name, resolved_from/resolved_to, etc.), giving the agent richer expectations than the schema alone.

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 starts with concrete user phrasings and states the verb+resource: 'full cross-source profile of a US public company in ONE parallel call.' It clearly differentiates from chaining individual SEC/XBRL/news lookups, and implicitly scopes to public companies (private companies are handled with a resolved:false response).

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?

Provides explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also explains when results are expected to be empty (e.g., FDA products for small-molecule/generic drug companies) and what happens for private companies, preventing misuse and misinterpretation.

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

B3.3/5.0
Disambiguation1/5

The tool set is a mix of 5 Google Calendar tools and 31 unrelated Pipeworx tools (e.g., ask_pipeworx, deep_research, entity_profile). Even within Pipeworx, tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have highly overlapping purposes, making it very difficult for an agent to distinguish which tool to use.

Naming Consistency1/5

Naming conventions are chaotic. The Google Calendar tools follow a consistent gcal_ prefix, but the rest use various patterns: pipeworx_*, polymarket_*, single words (remember, recall), and others (bet_research, scan_dependency). There is no overarching pattern.

Tool Count1/5

With 36 tools, the count is high, but only 5 are relevant to Google Calendar. The remaining 31 tools are from a completely different domain (Pipeworx data access, Polymarket betting, etc.), making the tool count severely inappropriate for the server's stated purpose.

Completeness2/5

For Google Calendar, the tools provide basic CRUD (create, get, list, search, list_calendars) but lack update and delete functionality. The vast number of unrelated tools does not compensate for these gaps. The overall surface is incomplete for the calendar domain.

Resources