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Glama

Monarch Initiative

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?

The description goes well beyond the annotations by disclosing soft-fail behavior for the USPTO PatentsView sunset, the expectation that companies with only small-molecule drugs will have an empty fda_products section, and the meaning of sources_used/sources_failed. It also explains that a private company returns resolved:false with a notes line rather than a bare failure. This is rich, non-obvious behavioral context that annotations alone could not provide.

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 and dense, but the front-loaded trigger phrases and 'ALWAYS PREFER' rule make key usage guidance immediately visible. The long tail enumerating sources and return fields is justified by the tool's complexity and the absence of an output schema. A slight structural improvement would be breaking the massive paragraph into sections, but every sentence carries non-redundant information.

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 compensates fully by enumerating returned fields, explaining empty-section semantics, covering soft-fail behavior, and stating exactly what happens for private companies. It also gives examples of all accepted value shapes and names the resolution mechanism. For a tool of this complexity, an agent has enough context to select it, invoke it, and interpret common edge-case results.

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 description coverage is 100%, so the baseline is 3 even without additional description. The description does repeat that type accepts 'company' or 'ticker' interchangeably and that value can be a ticker, CIK, or company name, but the schema already states these semantics nearly identically. It adds concrete examples and resolved_from/resolved_to behavior, but for parameter semantics specifically, the marginal value over the schema is limited.

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 specific task: produce a full cross-source profile of a US public company in one parallel call, with natural-language trigger examples. It clearly enumerates the resource (SEC EDGAR, XBRL, patents, contracts, FDA, H-1B, news, LEI) and the returned sections, making its scope and purpose unmistakable. This also differentiates it from single-source lookups and sibling tools like compare_entities or resolve_entity.

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 instructs 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' providing a direct selection rule. It also describes valid input shapes and the expected behavior for private companies, so an agent knows when the tool applies and what happens outside its coverage. The alternatives are named as a category rather than specific sibling tools, but the preference is unambiguous.

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

A3.7/5.0
Disambiguation4/5

Most tools have clear, distinct purposes, especially within the same domain (e.g., Polymarket betting tools each serve a specific function). However, the multiple data-querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) could cause confusion despite detailed descriptions.

Naming Consistency3/5

Many tools follow a verb_noun snake_case pattern (e.g., bet_research, compare_entities), but there are exceptions like ai_visibility_check, forget, and suggest_questions. The mix of imperative verbs and descriptive phrases creates inconsistency.

Tool Count2/5

With 35 tools, the server feels overloaded. While each domain (biomedical, financial, betting) is covered extensively, the sheer number of tools likely overwhelms agents, and many tools could be merged or split into separate servers.

Completeness4/5

The tool set is comprehensive for its declared purpose, covering biomedical queries, company data, betting analysis, memory management, and more. Minor gaps exist (e.g., no tool to delete a bet, no write operations for biomedical data), but the breadth is impressive.

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