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Glama

Data Michigan

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?

Beyond the readOnly/idempotent/openWorld annotations, the description discloses rich behavioral detail: one parallel fan-out, soft-failure of USPTO PatentsView, expected empty fda_products for non-biologic companies, 'empty section is a real no data, not a bug', and private-company resolution behavior. This materially reduces surprise for the calling agent.

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 the complexity of a multi-source fan-out tool justifies the length. It is front-loaded with purpose and usage, then proceeds through sources and output semantics in a structured way. A few example phrasings are redundant, but the density is appropriate.

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 document return structure, and it does so comprehensively: cik, resolved fields, recent_filings, fundamentals, patents, contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also covers failure modes and expected-empty sections, leaving no major operational gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and both type and value already carry thorough descriptions including ticker/CIK/name examples and zero-padding. The description adds more usage examples but does not materially extend parameter semantics beyond the schema; it mostly restates what the schema already documents.

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 and the verb 'profile': 'full cross-source profile of a US public company in ONE parallel call'. It is unambiguously distinct from chained single-source lookups and from sibling tools like compare_entities or resolve_entity because it explicitly limits scope to a holistic company profile.

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 states exactly when to prefer this tool ('when the user asks for a holistic view', 'Tell me about X', 'research Acme') and explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups'. It also covers the edge case where it should not be expected to succeed — private companies return resolved:false — which functions as a when-not signal.

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

A3.8/5.0
Disambiguation2/5

There are multiple severe overlap clusters. ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,578 tools, and ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly' — a direct ambiguity. The six polymarket_* tools plus bet_research form another dense, hard-to-distinguish cluster, and entity_profile/recent_changes/compare_entities/resolve_entity all have overlapping entity-investigation purposes. The long descriptions help but an agent would frequently misselect.

Naming Consistency3/5

The dominant families are internally consistent (polymarket_* prefix, ask_pipeworx_* suffix family, and the verb-based remember/recall/forget), which aids navigation. However, the overall set mixes several conventions: single-word nouns (query, datasets, metadata, recall), verb_noun compounds (validate_claim, generate_llms_txt), and domain_noun names (entity_profile, polymarket_edges). Readable, but there is no unified pattern across the server.

Tool Count2/5

34 tools is clearly over the 25-threshold for heaviness, and several earn little distinct value: ask_pipeworx_beta is a live duplicate of ask_pipeworx, the five-algorithm Polymarket family could be consolidated, and meta/utility tools (suggest_questions, discover_tools, pipeworx_trending, generate_llms_txt, scan_dependency) feel bolted on rather than essential. The breadth of the data domain justifies some size, but the redundancy and tangents push it into bloat.

Completeness3/5

Within its core sub-domains the surface is fairly complete: company research has resolve→profile/compare→recent_changes→validate_claim as a full lifecycle, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. The Polymarket workflow is especially thorough (detect→verify→fill-risk→track-decay). However, the server's stated identity ('Data Michigan') is barely served — the Michigan Open Data surface is only search/schema/query with no update or write path — and the scatter of unrelated tools (npm dependency scan, llms.txt generation) makes the overall purpose incoherent, so gaps are hard to evaluate.

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