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Glama

Mitre Cwe

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

A4.4/5.0
Behavior5/5

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

Annotations already mark it read-only/idempotent; the description then adds critical behavior: parallel fan-out across sources, USPTO soft-fail after sunset, expected empty fda_products for non-biologic companies, 'empty section means no data, not a bug', and resolved:false with notes for private companies. This is rich, non-obvious context that prevents misinterpreting responses.

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 complexity of a multi-source profile tool with fallbacks and edge cases justifies it. It front-loads the core purpose and then systematically covers fields and special cases; some wall-of-text parentheticals could be tightened, but each clause carries information.

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 fully enumerates the returned sections, source accounting, fallback behavior, and expected-empty semantics. It also covers all input forms and failure modes, leaving no obvious gap for a correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description mostly restates the schema's parameter semantics (ticker/CIK/name, both type values accepted). It adds minor operational color (name resolution via SEC EDGAR) but not substantial meaning beyond the schema, so baseline 3 is appropriate.

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 names the exact operation ('full cross-source profile of a US public company'), lists trigger queries, and scopes it to one parallel call. It distinguishes itself from single-pack SEC/XBRL/news lookups and from sibling resolution/profile tools by emphasizing the holistic cross-source output.

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?

It explicitly says to prefer this tool 'when the user asks for a holistic view' and over chaining single-pack lookups. It gives input-form guidance, but it does not name sibling alternatives or state scenarios where another tool should be chosen instead.

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

Only 7 of 38 tools are specific to CWE (weakness, children, parents, etc.), while the rest are generic data query, prediction market, and utility tools. This creates massive overlap and confusion: an agent looking for CWE data will encounter many unrelated tools with similar generic names like ask_pipeworx, deep_research, etc.

Naming Consistency2/5

CWE-specific tools use a consistent noun pattern (weakness, children, parents, etc.), but the remaining tools mix snake_case, camelCase, and no clear pattern (e.g., ask_pipeworx, bet_research, generate_llms_txt). The overall naming is inconsistent.

Tool Count1/5

38 tools is far too many for a CWE server. The vast majority are unrelated to CWE, making the tool surface bloated and unfocused. A concise set of ~5-10 CWE-specific tools would be appropriate.

Completeness3/5

The CWE-specific tools (weakness, category, view, children, parents, descendants, relationship) cover the main use cases for querying the CWE database. However, the server also includes many unrelated tools that dilute its purpose, making it feel incomplete for someone strictly needing CWE data.

Resources