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Glama

Gitlab

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

A4.3/5.0
Behavior5/5

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

Even though annotations already mark the tool as readable and non-destructive, the description adds significant behavioral context: USPTO PatentsView soft-fail, expected-empty fda_products, resolved:false with notes for private companies, and sources_used/sources_failed indicating 'no data' vs. bugs. This goes well beyond what the annotations or schema convey.

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 well-structured and front-loaded with concrete examples and the 'ALWAYS PREFER' rule. The detailed source-by-source breakdown and edge-case notes are justified for a complex multi-source tool, though a bit of redundancy with the schema remains.

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 what the agent will get back. It enumerates every returned section (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/sources_failed), and covers failure modes like empty sections, private companies, and soft-fails. Nothing essential 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 baseline is 3, but the description adds meaningful semantics: names resolve via SEC EDGAR's company-name match, CIK values should be zero-padded, and type='company'/'ticker' are interchangeable. These details enhance the parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description opens with concrete user phrasings and states 'full cross-source profile of a US public company in ONE parallel call,' clearly identifying the action and resource. It distinguishes itself from chaining single-pack lookups, though it does not explicitly differentiate from sibling holistic tools like deep_research or compare_entities.

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?

Explicitly tells agents to 'ALWAYS PREFER' this tool over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view, and explains what happens for private companies. It gives clear context but stops short of spelling out when to instead use sibling tools like compare_entities or deep_research.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, notably ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded which are near-identical in function. The polymarket_* family also has several members with closely related scopes, and the large number of data-query tools makes it hard to choose the right one without careful reading.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (list_subscriptions, validate_claim), others are noun-first (entity_profile, bet_research), and proper-noun prefixes like pipeworx_ and polymarket_ are used liberally. The gitlab_* tools follow a clear verb_noun pattern, but the rest of the set is mixed.

Tool Count2/5

With 36 tools, the server is overloaded, especially given that only 5 are GitLab-related while the rest are a sprawling data-access toolkit. Many tools could be consolidated (e.g., the ask_pipeworx variants), and the count exceeds what is reasonable for a focused GitLab server.

Completeness2/5

For a server named Gitlab, the coverage is severely incomplete: only list/get operations exist for projects, issues, and MRs, with no create, update, or delete capabilities. The broader data tools are more complete, but the nominal purpose of the server is clearly not fulfilled.

Resources