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Glama

fuiwanted

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already signal readOnly/openWorld/idempotent, but the description adds substantial behavioral context: it fans out across sources, soft-fails on the USPTO sunset, treats empty sections as real 'no data' rather than bugs, returns resolved:false for private companies, and reports sources_used/sources_failed. This goes well beyond the annotation hints.

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 much of the length is justified because there is no output schema to document the rich returned payload. It is front-loaded with trigger phrases and the key usage directive before enumerating sources and return behavior. It could be cleaner with structural formatting, but every sentence earns its place.

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 complexity, two required params, no output schema, and the breadth of sibling tools, the description covers inputs, return structure, source-specific failure semantics, empty-section behavior, and private-company handling. An agent has enough context to invoke it correctly without guessing.

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?

Schema coverage is 100%, so the baseline is 3. The description adds practical nuance beyond the schema by stating that 'company' and 'ticker' are interchangeable, that value can be a ticker/CIK/name, and that names resolve via SEC EDGAR company-name match. This is helpful but not a large departure from the schema's own detailed parameter 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?

The description states a specific verb and resource: it produces a 'full cross-source profile of a US public company' from a single parallel call. It is clearly differentiated from more narrow lookups by the 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' guidance and by enumerating the many data sources it fans out across.

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 gives explicit trigger examples ('Tell me about X', 'research Acme') and the condition 'when the user asks for a holistic view'. It also tells the agent to prefer this over chaining single-pack lookups, and explains edge behavior for private companies, making the decision to invoke it 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

A3.8/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded form a confusing cluster—beta currently behaves identically to the stable router. The six polymarket_* tools also share overlapping boundaries (edges vs. arbitrage vs. research vs. fill risk), requiring deep reading to select correctly.

Naming Consistency3/5

There are some coherent families (ask_pipeworx_*, polymarket_*, subscribe/unsubscribe/list_subscriptions, remember/recall/forget), but the overall style is mixed: verb-first names like compare_entities sit next to noun-first names like entity_profile and recent_changes. Everything is snake_case, so it is readable, just not driven by a single consistent convention.

Tool Count2/5

31 tools is too many for a single MCP surface, and each carries a very dense description. The server is essentially several different products bundled together: data access, deep research, prediction markets, AI visibility, memory, subscriptions, and utilities.

Completeness4/5

The data/research workflows are deeply covered: simple queries, grounded answers, deep research, entity resolution, comparisons, company profiles, recent changes, claim validation, and discovery. Minor gaps exist—no actual order placement for prediction-market trades, no subscription editing, and limited full-catalog browsing for the 5,596 underlying tools.

Resources