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Glama

Repology

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

A4.9/5.0
Behavior5/5

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

While annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, the description adds substantial behavioral context: fan-out across multiple sources, patent source soft-fail due to API sunset, expected-empty FDA sections, resolved_from/resolved_to, and the meaning of sources_used/sources_failed. It clearly communicates how the tool behaves beyond the structured annotation hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but densely packed with actionable information: trigger phrases, source lists, return fields, edge cases, and parameter guidance. Every sentence earns its place, and the opening line immediately anchors the agent with examples and the 'one parallel call' selling point.

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 burden of explaining returns — it enumerates result fields (cik, company_name, resolved flags, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) and covers failure semantics for private companies, empty sections, and soft-failing sources. An agent has enough context to invoke the tool correctly and interpret outcomes.

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?

The schema already documents both parameters at 100% coverage, but the description adds meaningful semantics beyond it: 'type' accepts 'company' or 'ticker' interchangeably, and 'value' can be a ticker, zero-padded CIK, or company name with name resolution. This is more guidance than baseline, though schema coverage means part of the job is already done.

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 task — building a full cross-source profile of a US public company in one call — and provides concrete trigger examples like 'Tell me about X' and 'brief me on Tesla'. It also distinguishes itself from single-pack lookups by explicitly saying 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view'.

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 gives direct when-to-use guidance: use when the user asks for a holistic view, and prefer over chaining cheaper single-source lookups. It also covers edge cases such as private companies returning resolved:false and FDA data being empty for non-biologic companies, so the agent knows what to expect.

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

B3.3/5.0
Disambiguation3/5

There is notable overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread). While descriptions differentiate them, agents may struggle to choose the appropriate one without careful reading.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (e.g., ask_pipeworx, compare_entities), but the Repology-specific tools break this pattern with simple nouns like maintainer, problems, project, and repositories. This inconsistency makes the overall naming feel mixed.

Tool Count2/5

With 36 tools, the set is too large for a server focused on Repology package queries. Many tools are from the Pipeworx platform and include redundant variants (e.g., ask_pipeworx_beta, polymarket_edge_tracker), inflating the count without adding substantial new functionality.

Completeness2/5

For a server named Repology, the tool surface is severely incomplete: it lacks fundamental Repology operations like detailed package comparisons, version history exploration, and repository-specific queries. Even as a general data platform, there are gaps such as no batch export or aggregate statistics.

Resources