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Glama

Pvgis

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

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, and the description builds on that with rich behavior: soft-fail on USPTO, empty sections meaning real absence of data, resolved:false handling for private companies, and source fan-out details. It tells agents what failure and empty results look like, which goes well beyond annotation defaults.

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 every section earns its place: query signals, required data fields, failure semantics, and input formats are all packed in. It is front-loaded with the core purpose and usage rule before the detailed field list, making the length acceptable for a complex multi-source tool.

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 explaining return values; it does so thoroughly with each result field (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) and semantics like empty-section-is-real-no-data. It also handles input resolution failures and expected absence of FDA products, so an agent can interpret results without additional context.

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 schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds meaningful semantics by explicitly saying `type` and `value` accept ticker, CIK, or name interchangeably, and by providing concrete examples for each form. This is helpful but not a huge leap over 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 opens with concrete user phrasings ('Tell me about X', 'research Acme', 'brief me on Tesla') and states the tool's exact function: a holistic cross-source profile of a US public company in one call. It is easily distinguishable from siblings by naming its coverage (SEC/XBRL/patents/contracts/FDA/H-1B/news/GLEIF) and by being explicitly preferred over chaining single-pack lookups.

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?

It explicitly tells the agent when to choose this tool: whenever the user asks for a holistic view or profile, and says ALWAYS PREFER it over chaining single-pack lookups. It also covers edge cases (private company behavior, ticker/CIK/name inputs), leaving little ambiguity about invocation.

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

B3.4/5.0
Disambiguation3/5

Multiple tools serve overlapping query purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish without careful reading. Similarly, prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges) have overlapping scopes.

Naming Consistency2/5

Names are highly inconsistent: verb_noun (ask_pipeworx, resolve_entity), noun_descriptive (entity_profile, polymarket_arbitrage), and simple nouns (tmy, pvgis). No clear pattern emerges, making it hard to anticipate tool names.

Tool Count3/5

34 tools is borderline high for a single server. While some utility tools (remember, forget) are justified, many tools are very narrowly scoped (tmy, generate_llms_txt) and could be merged or omitted.

Completeness3/5

The server covers a wide breadth (data querying, prediction markets, solar energy, AI visibility) but feels like a collection of unrelated domains. Core operations for data querying are present, but the solar tools (monthly_radiation, pv_performance) seem orphaned from the rest.

Resources