Skip to main content
Glama

Openweather

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 goes far beyond the readOnly/openWorld/idempotent annotations by disclosing fan-out behavior, fallbacks (GDELT→GNews), `sources_used`/`sources_failed` semantics, and the expected-empty fda_products case. It tells the agent that an empty section means real 'no data', not a bug, and that private companies return resolved:false with a notes line. No contradiction with annotations; this is model behavior transparency.

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 nearly every sentence earns its place given the tool's multi-source complexity. It is front-loaded with example intents and the preference rule, and structured into sources, return fields, and edge cases. Minor typographical artifacts and slight redundancy around `type`/`value` keep it from being a model of concision.

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 shape, and it succeeds: it enumerates fields (cik, recent_filings with URIs, fundamentals from the latest 10-K, patents, contracts, fda_products, hiring, news, LEI) and explains failure semantics. It even notes expected empty results for certain company types. The only minor omissions are precise formatting/pagination details, which are secondary for an agent deciding to call it.

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?

Even though the input schema already documents both parameters at 100% coverage, the description adds meaningful semantics: acceptable value shapes (ticker, zero-padded CIK, name), name resolution via SEC EDGAR, interchangeability of `type` values, and private-company behavior. This substantially exceeds the schema's enums and 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 concrete user intents and immediately states the tool's function: a full cross-source profile of a US public company in one parallel call. It explicitly contrasts this with chained single-pack lookups, making the tool's scope and value proposition clear. This is more than a vague verb+resource pairing; it tells the agent exactly what kind of request this satisfies.

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 gives an explicit preference rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies acceptable inputs and the private-company outcome. However, it does not name sibling alternatives like compare_entities or deep_research or state when NOT to use this tool, so some routing decisions are left to inference.

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

All Openweather 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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. However, 'ask_pipeworx' and 'ask_pipeworx_grounded' are very similar, and 'polymarket_arbitrage' and 'polymarket_edges' overlap in scope, which could cause confusion for an agent.

Naming Consistency3/5

Tool names mostly use snake_case but mix verb-first (e.g., 'compare_entities', 'resolve_entity') and noun-first (e.g., 'air_quality', 'entity_profile') patterns. Some names are single words ('forecast', 'geocode'), showing overall inconsistency.

Tool Count3/5

With 30 tools, the server covers many domains (weather, company info, betting, memory, subscriptions). While each tool serves a purpose, the count is on the higher side and could be streamlined, but it's not excessive given the diverse functionality.

Completeness4/5

The tool set provides comprehensive coverage for its domains: weather (current, forecast, air quality), company research (profile, compare, recent changes, validation), betting (research, arbitrage, edges), memory (CRUD), and subscriptions (CRUD). Minor gaps include no tool for editing subscriptions or deleting weather data, but these are outside the intended scope.

Resources