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Glama

Cbs Il

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?

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds rich behavioral context beyond those hints: it fans out across SEC, XBRL, USPTO, USAspending, FDA, DOL, news, and GLEIF; empty sections mean real absence of data; USPTO sunset soft-fails until reactivated; and FDA section absence is expected for small-molecule-only companies. This substantially prevents misinterpreting 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 long and dense, but almost every clause earns its place because it encodes return sections, soft-fail behavior, expected empty results, and parameter semantics. It is front-loaded with trigger examples and the core purpose. It could be slightly improved with bullet-point formatting, but it is not padded or redundant.

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, this description is unusually complete. It enumerates every returned section, explains `sources_used` / `sources_failed`, warns about expected empty sections, covers private-company behavior, and defines accepted value shapes. An agent has enough context to invoke the tool correctly and interpret its results without additional lookups.

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?

Schema coverage is 100%, but the description adds meaningful clarification: `type` accepts 'company' or 'ticker' interchangeably and both accept ticker, CIK, or company name. It also explains name resolution via SEC EDGAR and gives concrete examples like 'AAPL', '0000320193', and 'Moderna', which removes ambiguity beyond the schema.

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 uses specific verbs and resources: it is a 'full cross-source profile of a US public company in ONE parallel call' triggered by phrases like 'tell me about X' and 'brief me on Tesla.' It clearly distinguishes itself from single-pack SEC/XBRL/news lookups and from sibling research tools by emphasizing one-call holistic coverage.

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?

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also describes fallback and failure behavior, such as private companies returning resolved:false with a notes line instead of a bare failure, helping the agent decide when this tool is appropriate.

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

Several tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the polymarket_edges / polymarket_arbitrage / polymarket_kalshi_spread trio all scan for mispricings with overlapping descriptions. The rich usage notes help, but they cannot fully rescue a set where two tools literally do the same thing right now.

Naming Consistency3/5

The majority of tools use readable snake_case, but conventions are mixed: verb-first names (get_index_data, resolve_entity, validate_claim) sit alongside noun-first names (catalog_browse, index_catalog, entity_profile, bet_research), standalone verbs (remember, forget, subscribe), and adjective-led names (recent_alerts, deep_research). The pattern is predictable within clusters but not uniform across the set.

Tool Count2/5

35 tools is well above the comfortable range, and the set spans many unrelated domains—CBS Israel statistics, Pipeworx data routing, Polymarket betting, memory, subscriptions, npm dependency scanning, and AI visibility checks. Even if each tool has a purpose, the surface is bloated and poorly scoped for a server named 'Cbs Il'.

Completeness4/5

For a general data-access gateway, the set covers the major workflows: routing questions, grounded verification, deep multi-source research, entity profiles, comparisons, subscriptions, memory, and tool discovery. Minor gaps exist, such as no direct raw-record fetch without routing and no keyword search over the CBS catalog, but agents can work around these.

Resources