Skip to main content
Glama

Space Feeds

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?

The description provides extensive behavioral detail beyond the annotations: it enumerates the fan-out sources, explains that empty sections mean real 'no data,' discloses the USPTO patents API sunset and soft-fail behavior, and clarifies the resolved:false path for private companies. It also explains that `type` accepts values interchangeably. This far exceeds what the annotations alone convey.

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 nearly every sentence adds information relevant to correct invocation, and key guidance is front-loaded with examples and the 'ALWAYS PREFER' directive. The run-on semicolon-heavy structure makes it slightly harder to parse, but there is no filler. Given the tool's complexity, the length is justified.

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 carries the full burden of explaining return values, and it does so thoroughly: cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also addresses edge cases like empty sections and private companies. The description is complete enough for an agent to call this tool 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 coverage is 100%, but the description still adds significant meaning: it explains that 'company' and 'ticker' behave identically, gives examples for ticker, CIK, and name formats, notes zero-padding, and describes name resolution via SEC EDGAR. It also details the private-company response behavior. This is strong value beyond the schema.

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 phrasings ('Tell me about X', 'research Acme') and then states the tool builds a 'full cross-source profile of a US public company in ONE parallel call.' This clearly identifies the verb, resource, and scope, and distinguishes it from chaining individual lookups. It is specific and not a tautology.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving clear when-to-use guidance. It also specifies the accepted input forms and describes the private-company fallback behavior. It does not explicitly name sibling tools like compare_entities or deep_research as alternatives, but the guidance is otherwise unambiguous.

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

All Space Feeds 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

A3.6/5.0
Disambiguation2/5

Several tools occupy nearly the same niche: ask_pipeworx_beta is explicitly an identical duplicate of ask_pipeworx when no experiment is active, and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all overlap as question-answering entry points. Other families like entity_profile vs compare_entities vs recent_changes and ai_visibility_check vs scan_competitor_ai_presence also blur together despite long disambiguating descriptions.

Naming Consistency4/5

All tool names use a clean, readable snake_case style, and there are strong prefix families like ask_pipeworx, polymarket_, list_, and scan_. However, the set is not uniformly verb_noun: entity_profile, deep_research, recent_alerts, pipeworx_trending, and several others are noun phrases rather than actions, so the pattern is mostly consistent but not strict.

Tool Count2/5

34 tools is well beyond the 25+ threshold where a server starts feeling bloated, and the server name 'Space Feeds' suggests a narrow niche while most of the surface is a general data research, prediction-market, memory, and subscription platform. Each tool may be useful, but as a set the scope is sprawling rather than focused.

Completeness4/5

The major workflows have good lifecycle coverage: data lookup and grounded verification, entity resolution and profiling, prediction-market analysis, memory (remember/recall/forget), subscriptions (subscribe/list/unsubscribe/recent_alerts), and feed reading (list/read/fetch) are all represented. Minor gaps include a direct pipeworx:// citation reader and feed curation or management operations, but agents can work around those.

Resources