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Glama

Datecalc

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

A4.2/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive, and the description adds substantial behavioral context: parallel fan-out across sources, sources_used/sources_failed semantics, empty-section-is-real-no-data guidance, name resolution via SEC EDGAR, and resolved:false handling for private companies. This goes well beyond 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 front-loaded with trigger phrases and clear purpose, but the middle is a dense run-on enumeration of sources, return fields, and edge cases. Seven paraphrased examples at the start are redundant, and the overall structure would benefit from bulleted sections. Still, nearly every clause carries useful information, so it is acceptable despite being heavy.

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?

There is no output schema, so the description carries the full burden of documenting return values and edge cases. It covers cik, resolved_from/resolved_to, recent_filings with URIs, fundamentals, per-source sections, sources_used/sources_failed, expected empty sections, and private-company behavior. This is unusually complete for a tool with no structured output schema.

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 the baseline is 3, but the description adds meaningful detail: value accepts a ticker, zero-padded CIK, or company name; type values are interchangeable; and name resolution behavior plus the resolved:false result are clarified. It does not introduce new parameter meaning beyond that, but it reinforces and expands on the schema usefully.

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 states the tool's job: produce a full cross-source profile of a US public company in one parallel call, with examples showing when to trigger it. It distinguishes itself from chaining single-pack SEC/XBRL/news lookups, but it does not explicitly differentiate from close sibling tools like deep_research or compare_entities.

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?

The description gives an explicit preference rule: prefer this over chaining single-pack lookups when the user wants a holistic company view. It also documents the accepted input forms and the private-company fallback behavior. However, it does not state when NOT to use this tool, such as when a user wants a single filing or a detailed comparison between entities.

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

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, such as the multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) and the several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research). Memory tools (remember, recall, forget) also add to the confusion.

Naming Consistency3/5

Tool names are mostly snake_case but vary in pattern: some are verb_noun (add_duration, compare_entities), others are noun_noun (entity_profile, date_diff) or single verbs (forget). This mix reduces predictability but is not chaotic.

Tool Count2/5

The server name 'Datecalc' implies a narrow focus, yet 33 tools exist covering far more than date calculations. The count is too high for the implied purpose, and many tools are unrelated to the server's apparent domain.

Completeness2/5

For the implied date calculation domain, only three tools exist (add_duration, date_diff, date_info), leaving obvious gaps. For the actual broad data access domain, coverage is better but still lacks a cohesive structure.

Resources