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Glama

Colorado Information Marketplace

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

A4.7/5.0
Behavior5/5

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

Annotations already provide readOnly/openWorld/idempotent hints, and the description adds substantial behavior not in structured fields: parallel fan-out across many sources, fallback chains (GDELT→GNews, patents soft-fail), expected-empty semantics for fda_products, sources_used/sources_failed for distinguishing no-data from bugs, 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 dense — nearly every sentence adds operational detail (sources, output fields, fallbacks, edge cases). It front-loads with user queries and a clear statement of scope before enumerating the fan-out. It could be tightened, but it earns its length 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?

Given the tool's many sources and failure modes, the description is remarkably complete: it covers all input forms, the full return section list, expected empty sections, fallback/soft-fail behavior, and the private-company failure mode. With no output schema, this is exactly the context an agent needs to call and interpret results correctly.

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 description coverage is 100%, so the baseline is 3, but the description materially enriches both parameters: concrete examples ('AAPL', '0000320193', 'Moderna'), the zero-padded CIK requirement, name-resolution behavior, and an explicit note that 'type' values are interchangeable while the same value shapes apply. This goes well beyond the schema's enum and value descriptions.

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?

States a specific verb and resource: "full cross-source profile of a US public company in ONE parallel call" with illustrative natural-language examples. It distinguishes itself from siblings by emphasizing holistic coverage with 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' — clearly a different job than compare_entities, resolve_entity, or deep_research.

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?

Explicitly says when to prefer this tool ('ALWAYS PREFER... when the user asks for a holistic view') and contrasts it with single-pack lookups. It also defines the valid input space (ticker, CIK, or name) and the private-company edge case, though it does not explicitly enumerate when to choose sibling tools like compare_entities or resolve_entity.

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

A4/5.0
Disambiguation3/5

The tool set has several overlapping clusters: ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, entity_profile / recent_changes / compare_entities, and a half-dozen prediction-market tools. The descriptions are unusually detailed and mostly steer an agent correctly, but ask_pipeworx_beta is currently identical to ask_pipeworx and the prediction-market tools still require careful reading to pick the right one.

Naming Consistency3/5

All names are lowercase snake_case, but the conventions vary: many are verb_noun (resolve_entity, compare_entities, validate_claim), some are bare nouns (datasets, metadata, query), some are bare imperatives (remember, forget, subscribe), and there are separate prefix families like polymarket_* and pipeworx_*. It is readable and consistent in style, but not a single predictable pattern.

Tool Count2/5

34 tools exceeds the 25+ threshold for 'too many,' and the set is not tightly scoped: only datasets, metadata, and query directly relate to the stated Colorado Information Marketplace purpose. The bulk are Pipeworx data-research, prediction-market, memory, and subscription utilities, making the server feel like a broad platform bolted onto a state-data catalog.

Completeness4/5

For the core read-only lifecycle of the Colorado data catalog, search (datasets), schema inspection (metadata), and data retrieval (query) are covered. Minor gaps exist elsewhere: there is no explicit tool for fetching a pipeworx:// citation record directly, and some utilities like generate_llms_txt or scan_dependency are unrelated to the server's stated purpose, but most cited workflows can still complete.

Resources