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Glama

postcodes

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, and idempotent behavior, and the description adds substantial behavioral detail: one parallel call, soft-fail for the USPTO patents API, empty sections as real 'no data' not bugs, name resolution via SEC EDGAR, and private company behavior returning resolved:false with notes. This far exceeds what annotations provide.

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 the length is justified by the tool's complexity and the many edge cases an agent must understand. Examples and purpose are front-loaded, and each subsequent clause adds meaningful detail about sources, return fields, or failure semantics.

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?

There is no output schema, so the description carries the full burden of explaining what the tool returns. It enumerates every important result section, source failure behavior, input resolution behavior, and the semantics of empty results. No critical information needed to invoke the tool correctly is missing.

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 the schema already documents both parameters well. The description adds extra meaning beyond the schema by giving concrete examples of tickers, zero-padded CIKs, company names, and clarifying that type values are interchangeable.

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 states a specific, well-scoped purpose: a 'full cross-source profile of a US public company' produced in one parallel call, backed by many input examples. It lists the exact sources it fans out across and the return sections, which clearly distinguishes it from single-source lookup tools and siblings like 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?

It explicitly says to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving strong when-to-use guidance. It does not contrast itself with closely related siblings like compare_entities or deep_research, so the agent must infer some routing decisions for those alternatives.

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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping responsibilities: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all serve similar lookup purposes. entity_profile, compare_entities, and recent_changes all retrieve company data. Several Polymarket tools overlap in edge detection. The large number of tools with fuzzy boundaries makes it difficult for an agent to select the correct one.

Naming Consistency3/5

Tool names are a mix of conventions: some use verb_noun (lookup_postcode, validate_postcode, resolve_entity), others are verb_phrase (ask_pipeworx, deep_research, suggest_questions), and a few are compound (polymarket_arbitrage, scan_competitor_ai_presence). No uniform pattern, though the structure is readable.

Tool Count1/5

Despite being named 'postcodes', only 4 of 35 tools are directly about postcodes. The vast majority belong to a broad data platform (Pipeworx) with specialized tools for finance, betting, news, etc. The count is excessive for a focused service, and many tools are only useful for users of that platform, leading to clutter.

Completeness2/5

For a postcode server, the tools cover basic needs (lookup, nearest, random, validate). However, the server's actual scope is much larger; within that broader scope, there are notable gaps: no general text search, no direct access to raw SEC filings, and many tools depend on paid plans or external accounts. The coverage is uneven and incomplete for a unified data platform.

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