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Glama

Snb Ch

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

A4.8/5.0
Behavior5/5

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

Annotations already mark this read-only/idempotent/non-destructive, but the description adds substantial behavioral context beyond that: it fans out across multiple data sources, portions may soft-fail (USPTO patents sunset), empty sections are legitimate "no data" rather than failures, and sources_used/sources_failed reveal which sources worked. It also discloses that name resolution goes through SEC EDGAR and that a private company yields resolved:false instead of a bare error—exactly the kind of behavior an agent needs to interpret results correctly.

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 deliberately dense, front-loading the purpose and trigger examples before detailing output sections and edge cases. Every sentence adds value—the source fan-out, soft-fail note, empty-section expectation, and input-shape flexibility are all actionable. It is slightly longer than necessary for a single-purpose tool, but the complexity of the operation justifies the length.

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?

For a tool with no output schema and a complex multi-source operation, this description is remarkably complete. It covers input variants, output sections with formats (e.g., up to 5 recent_filings with URIs), failure semantics (patents soft-fail, private company behavior), and how to interpret empty sections. An agent selecting and invoking this tool has everything it needs to set expectations and parse the response correctly.

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. The description adds meaningful semantics beyond the schema: it explains that value can be a ticker, zero-padded CIK, or company name, that names resolve via SEC EDGAR, and that type and ticker are interchangeable and behave identically. This goes beyond the schema's descriptions by clarifying behavioral equivalence and edge cases, so a 4 is warranted.

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+resource: "full cross-source profile of a US public company" and distinguishes itself from chainable single-pack lookups with "ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups." It also enumerates concrete user phrasings ("Tell me about X", "research Acme", "brief me on Tesla"), leaving no ambiguity about what the tool does. It stands apart from siblings like resolve_entity or deep_research by scope and output.

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 prefer this tool over single-pack lookups when the user asks for a holistic view, and it gives exact trigger examples. It also clarifies edge-case usage: private companies return resolved:false with a notes line, and the type field accepts company/ticker interchangeably. This gives the agent clear when-to-use and when-not-to-use guidance without naming alternatives aloud.

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

A3.9/5.0
Disambiguation3/5

Several tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and suggest_questions all handle question-answering, with beta currently identical to the main router. The Polymarket family has six distinct but related tools, and descriptions are detailed enough to differentiate, but an agent could still misselect when the boundaries are subtle.

Naming Consistency3/5

All tool names use snake_case, but the pattern is mixed: some start with verbs (resolve_entity, validate_claim), while many are noun phrases (entity_profile, bet_research, polymarket_edges). This is readable but not a consistent verb_noun convention, and the repeated ask_pipeworx_* and polymarket_* prefixes add some structure.

Tool Count2/5

At 33 tools, the surface is heavy for an agent to navigate. While the server covers a broad data-research and prediction-market domain, the count exceeds what is typically manageable, and several tools (ask_pipeworx_beta, discover_tools) could be collapsed or merged without losing core capability.

Completeness4/5

The tool set covers the domain well: querying, grounded verification, entity profiles, comparisons, prediction-market analysis, subscriptions, memory, and onboarding. Minor gaps exist (e.g., no tool to fetch a full SEC filing body directly, relying on ask_pipeworx routing), but there are no obvious dead ends for the stated purposes.

Resources