Skip to main content
Glama

Data Fcc

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?

Annotations already signal read-only, open-world, idempotent behavior; the description adds substantial beyond-annotation context: the parallel fan-out across sources, `sources_used`/`sources_failed` semantics, empty sections meaning real absence of data, USPTO API sunset soft-fail behavior, and FDA Purple Book limitation to biologics. No contradictions with annotations are present.

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 it earns its length for a tool this complex: it front-loads the purpose and preference rule before enumerating output fields and failure semantics. There is some redundancy with the schema descriptions, but each section adds operational context an agent needs. It is verbose but not padded.

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 two simple params, no output schema, and rich behavioral complexity, the description covers accepted input forms, output structure, source behaviors, empty-section semantics, soft-failures, and the private-company edge case. An agent has everything needed to select and invoke the tool correctly.

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 parameters are described accurately in the schema. The description restates that `type` and `value` accept ticker, CIK, or company name interchangeably, adding no meaningful semantic detail beyond the schema. The mention of resolved_from/resolved_to and company-name resolution describes return behavior, not parameter semantics, so baseline 3 is appropriate.

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 query examples and defines the operation as producing a 'full cross-source profile of a US public company in ONE parallel call.' It clearly identifies the resource (SEC EDGAR, XBRL, patents, contracts, FDA, H-1B, news, GLEIF) and explicitly differentiates itself from chaining single-pack lookups. This distinguishes it from siblings like compare_entities and 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which provides clear when-to-use guidance. It also notes when a private company results in resolved:false. However, it does not explicitly state when to use alternatives like compare_entities or resolve_entity instead, so it falls just short of full exclusionary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Fcc toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation3/5

Several tools overlap in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, and validate_claim all query the same underlying data catalog in similar ways. The descriptions provide detailed usage guidance, but the query family and the five-strong Polymarket family still create real misselection risk. Memory, subscription, and FCC tools are clearly distinct.

Naming Consistency4/5

Tool names are consistently snake_case and mostly follow recognizable verb_noun or prefixed-family patterns (ask_pipeworx_*, polymarket_*, pipeworx_*). The main deviations are bare one-word names like datasets, metadata, query, remember, and forget, plus a few noun-first names like entity_profile and polymarket_arbitrage. Overall the naming is readable and predictable, with only minor inconsistencies.

Tool Count2/5

At 34 tools, this server is well past the 25+ threshold and feels overloaded. It bundles a general-purpose data-query gateway, FCC open-data access, Polymarket analytics, AI-visibility scanning, memory, subscriptions, npm dependency checks, and llms.txt generation into one surface. Each functional area is small on its own, but the combined set would be more coherent split into several focused servers.

Completeness4/5

The core data lifecycle is well covered: discovery (suggest_questions, discover_tools), single queries (ask_pipeworx), grounded/evidence-backed answers, deep multi-source research, entity resolution/profiling/comparison, change feeds, and fact-checking all exist. FCC open data has search, schema, and query tools, and subscriptions/memory have full CRUD-style coverage. Minor gaps remain (e.g., no direct pipeworx:// URI fetch tool, no enumeration of all discoverable data sources), but they do not block typical workflows.

Resources