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Glama

Wayback

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

A4.6/5.0
Behavior5/5

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

Because annotations already declare read-only, idempotent, and non-destructive behavior, the description adds substantial extra context where it matters: it explains source fan-out, GDELT→GNews fallback, sources_used/sources_failed semantics, why an empty section is not a bug, and how private companies resolve to resolved:false with a notes line. This goes well beyond what annotations alone 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 tool is complex and the length is mostly earned by concrete behavioral details. It is front-loaded with trigger phrases and usage intent, though the return-value enumeration is dense and somewhat run-on, which costs a 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?

There is no output schema, so the description must stand in for it, and it does: accepted inputs, resolved name behavior, private-company behavior, the list of sources, key returned fields, source-status fields, and fallback semantics are all covered. An agent has enough to call the tool correctly and interpret its results in most cases.

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 baseline is 3, and the description genuinely adds value by showing that type and value are interchangeable, that value can be a ticker, CIK, or company name, and that names resolve via SEC EDGAR. It reinforces and clarifies the schema rather than merely repeating it.

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 verb and resource: it builds a full cross-source profile of a US public company in one parallel call. It also distinguishes itself from chaining single-source SEC/XBRL/news lookups, which is exactly the sibling-confusion an agent needs resolved.

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 explicitly tells the agent when to use it — whenever the user asks for a holistic view or says things like "tell me about X" or "brief me on Tesla" — and says it should be preferred over chaining single-pack lookups. It does not name specific sibling tools as exclusions, so it stops just short of full when-not-to-use guidance.

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

A3.5/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in purpose, and the five polymarket_* tools all target prediction-market analysis. The presence of a broad Pipeworx data layer alongside Wayback-specific tools under one server name further blurs boundaries.

Naming Consistency3/5

Most tools use snake_case, but the pattern varies: many follow verb_noun (get_snapshot, list_snapshots, resolve_entity, validate_claim), while others are noun_noun (entity_profile, polymarket_edges, pipeworx_feedback, bet_research). Suffixes like _beta and _grounded are used inconsistently, and some names are long and descriptive while others are terse.

Tool Count2/5

34 tools is far more than what a Wayback Machine server should need, and most tools (Pipeworx data queries, prediction-market analysis, memory, subscriptions) have nothing to do with the Wayback Machine. The server's stated purpose appears narrow, but the tool set is bloated with unrelated functionality.

Completeness2/5

For the Wayback Machine domain, only three tools are relevant (get_capture_count, get_snapshot, list_snapshots), and obvious operations like saving/archiving a URL, comparing snapshots, or handling deleted captures are missing. The Pipeworx-related tools are broad but not clearly aligned with the Wayback theme, so the core purpose is under-served.

Resources