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Glama

Datalastic

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?

Beyond the read-only/idempotent annotations, the description discloses substantial behavioral detail: source fan-out, soft-failure of USPTO, empty-section semantics, resolved_from/resolved_to behavior, and the explicit note that an empty fda_products section is expected rather than a bug. This gives the agent an accurate mental model of what to expect.

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 unusually information-dense, and the most important guidance is front-loaded. Some sentences pack multiple concepts and could be broken into structured bullets, but every clause earns its place given the tool's complexity.

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 serves as the sole source of return-value documentation. It enumerates all returned sections, explains sources_used/sources_failed semantics, covers expected empty fields, and documents failure modes. Nothing an agent needs to call this tool correctly is missing.

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?

Even though input-schema coverage is 100%, the description enriches both parameters: it explains that 'type' accepts company/ticker interchangeably, that 'value' can be a ticker, zero-padded CIK, or company name, and how name resolution works. This materially reduces ambiguity.

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 clearly identifies the resource ('US public company') and the precise operation ('full cross-source profile ... in ONE parallel call'). It also distinguishes itself from chained single-pack lookups by explicitly directing agents to prefer this tool for holistic research requests.

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?

The description provides explicit usage guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also lists concrete trigger phrases and explains behavior for edge cases like private companies, which helps 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

A3.9/5.0
Disambiguation2/5

Several tool clusters have genuinely fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grouned, and deep_research all route to the same 5,767 tools and differ only by use-case nuance, while polymarket_edges, polymarket_arbitrage, and bet_research all surface trading opportunities. scan_competitor_ai_presence is a thin wrapper over ai_visibility_check, and entity_profile, recent_changes, and compare_entities pull overlapping company data. The descriptions are detailed, but an agent can easily select the wrong tool in these clusters.

Naming Consistency4/5

All tools use snake_case and family prefixes are consistent (polymarket_*, pipeworx, datalastic_*, scan_*, ask_*), making the set predictable and readable. The main deviation is verb placement — verb-first (list_subscriptions, resolve_entity, search_within) vs noun-first (entiy_profile, recent_alerts, bet_research) — and prefix position varies between ask_pipeworx and pipeworx_feedback, but these are minor.

Tool Count2/5

33 tools exceeds the heavy threshold, and the count is padded by redundancy: four ask_pipeworx variants that are near-identical, six polymarket tools with overlapping scans, and wrapper tools like scan_competitor_ai_presence that just call ai_visibility_check. The server name suggests maritime focus but only two tools serve that domain, while the rest span a sprawling data-research, prediction-market, AI-visibility, and npm-scanning surface. Consolidating the ask_pipeworx family into one router with a mode parameter and merging wrappers would trim the set to roughly 20 tools without losing capability.

Completeness4/5

The core data-research lifecycle is thoroughly covered: resolve_entity feeds entity_profile, compare_entities, recent_changes, validate_claim, and deep_research, and the prediction-market workflow includes discovery, edge detection, fill-risk validation, and cross-venue analysis. Subscriptions, memory, and feedback are well supported. Minor gaps exist — the datalastic maritime piece has only live position lookups (no history or fleet tools), and one-offs like generate_llms_txt and scan_dependency feel unrelated — but there are no critical dead ends.

Resources