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Glama

Futuur

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

A4.4/5.0
Behavior5/5

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

The description goes far beyond the readOnly/idempotent annotations by documenting real behavior: fanning out across many data sources, soft-failing on the sunset USPTO endpoint, expected empty results for small-molecule-only FDA companies, resolved:false for private companies, and sources_used/sources_failed semantics. These caveats prevent an agent from misinterpreting empty sections as errors.

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 front-loaded with examples and the core directive, and it packs a lot of necessary output detail because there is no output schema. It is slightly dense and repeats input-format facts already present in the input schema, so it is not maximally concise.

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?

For a high-complexity aggregator with no structured output schema, the description is remarkably complete: it enumerates sources, return-field semantics, fallbacks, failure conventions, input shapes, and edge cases. An agent has everything needed to invoke it correctly and interpret both populated and empty 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 coverage is 100%, so the baseline is 3, and the description mostly restates what the schema already says about `type` and `value` (ticker/CIK/name, interchangeable type). It adds slight practical color with resolved_from/resolved_to behavior, but that is output behavior rather than new input-parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific verb+resource: a one-call, cross-source profile of a US public company, with representative user utterances. It advertises itself as the preferred alternative to chaining individual lookups, but it does not explicitly distinguish itself from sibling tools like resolve_entity or compare_entities, so it stops short of full sibling differentiation.

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 states when to use it — 'ALWAYS PREFER ... when the user asks for a holistic view' — and what to avoid: chaining single-pack SEC/XBRL/news lookups. It also explains how different inputs are handled, including the private-company fallback, leaving little ambiguity about call intent.

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
Disambiguation2/5

Multiple tools have overlapping or near-identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions with varying degrees of grounding. The polymarket_* family (edges, arbitrage, fill_risk, edge_tracker) and bet_research also create boundary confusion. An agent would struggle to consistently select the correct tool without deep reading of each description.

Naming Consistency3/5

Most tools follow a verb_noun snake_case pattern (search_markets, get_market, compare_entities, resolve_entity), but there are clear outliers like pipeworx_trending, recent_alerts, top_markets, and pipeworx_feedback which use adjective_noun or noun_adjective forms. The pattern is mostly consistent but has enough deviations to feel mixed.

Tool Count2/5

With 34 tools, the surface is decidedly heavy. Many tools serve entirely different domains (memory, subscriptions, AI visibility, npm dependency scanning, cross-venue arbitrage) rather than a unified purpose. This feels like several server concepts merged into one, making the count inappropriate for a single coherent server.

Completeness3/5

The data-research side is fairly comprehensive: querying, grounding, comparisons, profiles, claim validation, entity resolution, change tracking, and memory are all covered. However, for the nominal Futuur prediction-market domain, only read-only market lookup exists — no trading, account management, or order placement. Subscription CRUD is also missing an update operation, and several research tools only cover US public companies and specific data sources.

Resources