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Glama

Horizons Nasa

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

A4.8/5.0
Behavior5/5

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

Annotations already signal read-only, open-world, idempotent, and non-destructive. The description adds genuinely useful behavior beyond that: USPTO PatentsView soft-fails after sunset, fda_products being empty is expected for non-biologic companies, sources_used/sources_failed distinguish real no-data from bugs, and name resolution behavior is explained.

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 for a tool with no output schema and many source-specific behaviors, nearly every clause earns its place. The trigger phrases are front-loaded and the source-by-source breakdown is dense rather than padded, though it could be tightened with clearer sectioning.

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 and the absence of an output schema, the description covers input variations, output field names, source lists, fallback behavior, failure semantics, and the meaning of empty sections. An agent has enough context to select the tool and anticipate its response shape.

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 baseline is 3, but the description adds resolution semantics and output implications: names resolve via SEC EDGAR, both type values are accepted interchangeably, and resolved_from/resolved_to appear when a name was passed. This goes beyond the schema's raw 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 opens with natural-language triggers and a precise statement: 'full cross-source profile of a US public company in ONE parallel call.' It then enumerates the exact data sources covered, making it unambiguous what the tool produces and how it differs from narrow single-source lookups.

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 says to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' naming the alternative pattern it replaces. It also covers edge-case usage: private companies return resolved:false with a notes line rather than a bare failure.

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

A4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose with detailed descriptions that differentiate overlapping capabilities (e.g., ask_pipeworx vs deep_research vs ask_pipeworx_grounded vs bet_research). No two tools appear redundant; even similar prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have specific scopes.

Naming Consistency5/5

Tool names consistently use lowercase snake_case (e.g., ai_visibility_check, compare_entities, pipeworx_trending, polymarket_kalshi_spread). Single-word exceptions (ephemeris, lookup, observers, recall, remember, vectors) are common short verbs and do not break the pattern. No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is above the typical 3-15 range, but the server is a comprehensive data platform covering multiple domains (SEC, FDA, FRED, prediction markets, memory, subscriptions, feedback). The count is justified given the breadth; it feels slightly heavy but not bloated or redundant.

Completeness5/5

The tool surface covers the full lifecycle for a data/research platform: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookups (ask_pipeworx, entity_profile), comparison (compare_entities), validation (validate_claim), prediction-market operations (polymarket_*), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), and meta/feedback (pipeworx_feedback, pipeworx_trending). No obvious gaps for typical workflows.

Resources