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Glama

L2beat

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds substantial behavioral detail beyond those: it explains soft-failure for patents, GDELT→GNews fallback, FDA Purple Book expectations, empty sections meaning real absence of data, and private-company non-failure handling. It also lists the exact return sections, making behavior highly transparent.

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 structured: examples first, then purpose, then source fan-out, then return sections, then input handling and edge cases. Every sentence earns its place, and the complexity of the tool justifies the length. The front-loading of user phrasings and the 'ALWAYS PREFER' directive makes it immediately actionable.

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 carries the full burden of explaining return values. It does so thoroughly, covering cik, company_name, resolved flags, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also explains soft-failures and no-data semantics, making the tool fully usable without additional documentation.

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 input schema covers 100% of parameters with clear descriptions, so the baseline is 3. The description adds value by emphasizing that type and value are interchangeable, names resolve via SEC EDGAR, CIKs should be zero-padded, and private companies get a resolved:false result. This goes beyond schema wording, though it does partly restate 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 opens with natural-language triggers and clearly states the tool builds a 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes itself from chained single-source lookups and sibling tools like resolve_entity, compare_entities, and deep_research by naming its specific aggregation behavior. This is a specific verb-plus-resource statement with strong sibling differentiation.

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 to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a clear when-to-use rule. It also enumerates input types and handles edge cases like private companies returning resolved:false. This is explicit guidance with concrete 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

A3.6/5.0
Disambiguation2/5

The tool set mixes L2beat-specific tools with a large number of unrelated Pipeworx, Polymarket, and other domain tools. While individual descriptions are clear, the overall set forces agents to navigate many irrelevant tools, causing confusion about the server's primary purpose.

Naming Consistency2/5

Tool names follow inconsistent conventions: snake_case (list_projects), camelCase (ask_pipeworx), and mixed patterns (generate_llms_txt, bet_research). No unified naming pattern exists across the set, reducing predictability.

Tool Count1/5

With 35 tools, the count is far too high for an L2beat server. Only about 5-6 tools are actually related to L2 scaling; the rest are from other domains, making the tool surface bloated and unfocused.

Completeness2/5

The L2beat-specific tools cover basic operations (list projects, get project, TVS, activity) but lack important features like bridge analysis, project comparisons, or detailed risk breakdowns. The numerous unrelated tools do not address gaps in L2 functionality.

Resources