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Glama

Sefaria

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond those annotations: it fans out in one parallel call, soft-fails on the USPTO sunset, uses GDELT→GNews fallback, reports per-source success/failure via sources_used/sources_failed, and can resolve a name to a CIK. It also explains that an empty section is real 'no data', not a bug. It does not claim anything contradicting the annotations. It could gain the last point by stating rate-limit or latency expectations, but the parallel-call behavior is already a strong extra disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense and front-loaded with user intents, which is good. However, it is quite long and packs a lot of inline exception handling into a single paragraph (USPTO soft-fail, GDELT→GNews fallback, FDA semantics, private-company behavior), making it harder to scan quickly. It earns its place for the most part, but the density is borderline and some details could be moved to the schema or summarized.

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 2 parameters, 100% schema coverage, and no output schema, the description is remarkably complete. It enumerates the major return components, explains per-source failure reporting, documents input resolution behavior, and explicitly handles edge cases like private companies and FDA-empty results. An agent can decide when to call it, how to populate `value`, and how to interpret the response without external documentation.

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%, so the schema already fully documents both parameters. The description adds a small amount of complementary meaning: it emphasizes that `type` accepts 'company' or 'ticker' interchangeably and that the same `value` shapes apply to either, and it gives concrete examples (AAPL, 0000320193, Moderna). That is somewhat useful but largely restates the schema. Baseline 3 is appropriate because the schema already does the heavy lifting.

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 concrete user phrasings ('Tell me about X', 'research Acme', 'brief me on Tesla') and then states a specific verb+resource: builds a full cross-source profile of a US public company in one parallel call. It explicitly names the fan-out destinations (SEC EDGAR, XBRL, USPTO patents, federal contracts, FDA biologics, H-1B, news, GLEIF), which distinguishes it sharply from sibling tools like resolve_entity, deep_research, or get_text. The scope is unambiguous.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' — that is a direct when-to-use directive with a stated alternative. It also tells the agent which input shapes are accepted (ticker, zero-padded CIK, company name), and clarifies expected behavior for private companies (resolved:false with a notes line). It even explains what is NOT a failure (empty fda_products for small-molecule-only companies), which helps the agent avoid false alarms.

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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges). The Sefaria-specific tools are few, making it hard to distinguish between the core functionality and auxiliary utilities.

Naming Consistency2/5

Tool names follow inconsistent conventions: some use verb_noun (ask_pipeworx, get_text), some noun_verb (ai_visibility_check, bet_research), and some are standalone names (pipeworx_feedback, polymarket_edges). This mix of patterns makes the set appear haphazard.

Tool Count1/5

With 33 tools, the server is heavily overloaded, especially given the name 'Sefaria' which implies a focused set for Jewish text access. The vast majority of tools are unrelated to Sefaria, belonging to the Pipeworx ecosystem, making the count inappropriate for the server's stated purpose.

Completeness1/5

For a server named Sefaria, the tool surface is severely incomplete. Only three tools (get_text, get_commentaries, lookup_ref) directly relate to Jewish texts, missing fundamental operations like search, list books, or manage content. The remaining 30 tools belong to other domains, leaving the core domain under-served.

Resources