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Glama

Numbers

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?

It discloses substantial behavior beyond the annotations: parallel fan-out across multiple sources, resolved_from/resolved_to behavior, source failure semantics, empty sections meaning real no-data, private-company handling, and interchangeability of type values. Annotations already mark it read-only/idempotent, and the description adds rich operational context without contradicting them.

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 information-dense; nearly every sentence carries useful selection or invocation guidance. The example-query list is front-loaded, and the output/edge-case details are grouped logically. It would benefit from light restructuring to reduce run-on feel, but there is minimal fluff.

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 does the necessary work of explaining return components: cik/company_name, resolved_from/resolved_to, recent_filings with URIs, fundamentals fields, source list and failure reporting. It also covers accepted input forms and the private-company edge case, making it complete enough for an agent to invoke confidently.

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%, and the schema already documents ticker/CIK/name and type interchangeability. The description adds meaningful nuance beyond the schema by describing resolution behavior, resolved:false for private companies, and the explicit notes line, so it earns credit above baseline.

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 concrete function: build a full cross-source profile of a US public company in one parallel call. It opens with many natural-language trigger examples and explicitly contrasts itself with single-pack SEC/XBRL/news lookups, making its identity and differentiation clear.

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 says to prefer this tool over chaining single-pack lookups when the user wants a holistic view. It does not name specific sibling tools or state when-not-to-use cases, so it is slightly less complete than a definition with explicit exclusions and named 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

A3.8/5.0
Disambiguation3/5

Most tools have detailed descriptions that clearly delineate their purposes, but ask_pipeworx_beta is currently functionally identical to ask_pipeworx, and deep_research overlaps with ask_pipeworx for multi-part questions. The six polymarket_* tools also share related territory, though their boundaries are well-documented.

Naming Consistency3/5

All names use snake_case, and domain prefixes like polymarket_, pipeworx_, and ask_ add predictability. However, the set mixes verb-led names (compare_entities, resolve_entity, search_within) with noun-led names (entity_profile, recent_changes, bet_research), so there is no single consistent verb_noun convention.

Tool Count2/5

35 tools is well above the 15-25 'heavy' band and feels like a kitchen sink: the server bundles a data-research platform, prediction-market analysis, memory, subscriptions, number conversions, and web-dev utilities into one surface. Many of these are unrelated to the server's 'Numbers' identity.

Completeness4/5

The main subdomains are thoroughly covered: data lookup (ask_pipeworx variants, deep_research, validate_claim), prediction-market analysis (arbitrage, edges, fill risk, kalshi spread), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/recent_alerts). Minor gaps exist, such as no subscription update operation, but core lifecycle coverage is strong.

Resources