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Glama

Pypi

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, so the description carries a lower burden, but it still adds substantial behavioral context: the fan-out across SEC, XBRL, USPTO, USAspending, FDA, DOL, news, and GLEIF; the soft-fail for the USPTO API sunset; the expectation that an FDA section may legitimately be empty; and the meaning of sources_used/sources_failed. This goes well beyond the structured metadata.

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 front-loaded with examples and the 'ALWAYS PREFER' directive, then systematically enumerates the data sources and return sections. There is minor redundancy around input forms at the end, but the length is justified by the tool's complexity and the absence of an output schema.

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, no output schema, and two open-ended parameters, the description covers all necessary call decisions: input forms, accepted type values, returned fields with caveats (patent sunset, FDA empty-section expectation, H-1B metrics), behavior for private companies, and how to interpret empty results. An agent could invoke this tool correctly with only the description.

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%, and the description adds value beyond the schema by clarifying that 'value' accepts a ticker, zero-padded CIK, or company name interchangeably, that type='company' and type='ticker' behave identically, that names resolve via SEC EDGAR's company-name match, and how unresolved private-company cases are reported. This is more than the schema alone provides.

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 natural-language examples ('Tell me about X', 'research Acme', 'brief me on Tesla') and states the resource: a full cross-source profile of a US public company in one parallel call. It clearly distinguishes itself from single-pack lookups and sibling tools by the directive 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news 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?

The description explicitly says when to prefer this tool, names the alternative pattern it replaces ('chaining single-pack lookups'), and specifies accepted input forms — ticker, CIK, or company name. It also preempts common confusion: private companies yield resolved:false with an explicit notes line, and 'company'/'ticker' types are interchangeable.

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

A3.7/5.0
Disambiguation2/5

Several tools have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently exactly the same behavior, and polymarket_edges, polymarket_arbitrage, and bet_research all present as 'find a betting opportunity' scanners. The detailed descriptions help, but an agent still needs careful triage to avoid picking the wrong entry point.

Naming Consistency3/5

The set is uniformly snake_case and many tools follow verb_noun (get_package, list_releases, resolve_entity), but conventions are split between product-prefixed families (ask_pipeworx, pipeworx_feedback, polymarket_*), noun-led names (entity_profile, recent_alerts, deep_research), and bare-verb memory tools (remember, recall, forget). The result is readable but not a single predictable pattern.

Tool Count2/5

35 tools is well into the overgrown range, and the count is especially mismatched for a server labeled Pypi: only a handful of tools actually relate to Python packages, while the rest cover Pipeworx data lookup, prediction markets, AI visibility, memory, and subscriptions. This looks like several unrelated tool surfaces merged under one server.

Completeness3/5

For the broad Pipeworx data-research surface, coverage is strong: lookup, grounded answers, deep research, entity profiles, comparisons, validation, subscriptions, and memory are all represented. However, for a PyPI-focused server the surface is incomplete—there is no package search, upload, or account/maintainer tooling—and the PyPI tools feel like an afterthought.

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