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Glama

Ssb No

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

A4.6/5.0
Behavior5/5

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

Even with readOnly/openWorld/idempotent annotations, the description adds substantial behavioral detail: fan-out across many sources, exact return sections, sources_used/sources_failed semantics, patents soft-fail, FDA empty-section expectations, and private-company resolved:false behavior. No contradiction with annotations.

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 it front-loads the key directive ('ALWAYS PREFER') and packs every clause with non-obvious behavioral details rather than fluff. It earns its length for a complex multi-source tool, though the dense single-paragraph structure could be marginally better organized for quick scanning.

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 must explain the return payload itself, and it does so thoroughly: fields, sources, failure semantics, expected empty sections, and resolution behavior. Inputs, edge cases, and fallback behavior are all covered, making the description sufficient for an agent to call the tool correctly.

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?

The input schema already documents type and value with 100% coverage, so the baseline is 3. The description adds beyond that by showing concrete accepted shapes (ticker, zero-padded CIK, name), explaining name resolution via SEC EDGAR, and clarifying that private companies return resolved:false rather than failing. Slight redundancy exists, but the added examples and edge-case semantics merit a 4.

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 a concrete purpose: 'full cross-source profile of a US public company in ONE parallel call.' It gives many natural-language trigger examples and explicitly frames itself as the preferred holistic alternative to chaining single-pack SEC/XBRL/news lookups, which differentiates it from narrower research tools.

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 clearly says to use this tool when the user asks for a holistic view and explicitly prefers it over chaining single-pack lookups. However, it does not provide exclusions or directly compare against sibling tools like compare_entities, deep_research, or resolve_entity, so it stops short of full when-to-use/when-not-to-use guidance.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with detailed descriptions that explain when to use which. Overlapping tools like ask_pipeworx vs ask_pipeworx_grounded are explicitly differentiated by use case (casual vs high-stakes). The Polymarket tools are highly specialized and non-overlapping.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive terms (e.g., ask_pipeworx, resolve_entity, validate_claim). There is no mixing of camelCase or other conventions, making the names predictable and easy to parse.

Tool Count4/5

With 33 tools, the count is above the typical 3-15 range, but it is justified by the server's broad scope covering multiple domains (SEC, FDA, FRED, prediction markets, etc.) and includes meta-tools for discovery and monitoring. Each tool seems necessary for the overall functionality.

Completeness5/5

The tool surface covers a comprehensive range of operations: data querying, entity profiles, comparisons, monitoring, memory, search, and even feedback. It includes both general-purpose and specialized tools, leaving no obvious gaps for the stated purpose of authoritative data retrieval and analysis.

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