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Glama

Blockchair

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 cover safety (read-only, idempotent, non-destructive), and the description adds substantial behavior beyond that: parallel fan-out across external sources, soft-fail on USPTO, expected-empty FDA sections, sources_used/sources_failed, and private-company resolved:false behavior. It even prevents misinterpretations of empty data. No contradiction with annotations.

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 length is earned by the tool's breadth: examples, source fan-out, return fields, caveats, and input modes. It front-loads the core behavior and preference rule before diving into detail. A few example strings and the final parameter note are redundant with the schema, keeping it just shy of a 5.

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?

Given there is no output schema, the description compensates by enumerating every return bucket (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/failed) plus edge-case behavior. For a high-complexity tool, nothing essential an agent needs to confidently invoke it is missing.

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?

The input schema covers both parameters at 100% and already documents ticker/CIK/name inputs and type interchangeability. The description repeats essentially the same parameter semantics, adding only output-context notes like resolved_from/resolved_to. Since the schema carries the full parameter documentation burden, the 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 opens with concrete example queries and immediately defines the operation: a single parallel call that returns a full cross-source profile of a US public company. It explicitly contrasts itself with chaining single-pack lookups, so an agent can tell it apart from narrower research tools. The enumeration of return sections and accepted identifiers makes the tool's scope unmistakable.

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 states an explicit preference rule: ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. It also gives behavioral guidance around private companies, name resolution, and expected-empty sections. It lacks a formal 'don't use for X' list, but the main selection context is clearly established.

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

B3.2/5.0
Disambiguation2/5

Several tight clusters of overlapping tools: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer factual questions (beta is currently identical to stable per its own description), six Polymarket tools all surface betting/edge opportunities, and ai_visibility_check vs scan_competitor_ai_presence duplicate functionality. Despite long descriptions, an agent could easily pick the wrong tool.

Naming Consistency2/5

Naming mixes bare single-word nouns (address, block, node, stats, transaction), bare verbs (remember, recall, forget, subscribe), verb_noun compounds (generate_llms_txt, scan_dependency, compare_entities), and prefixed families (polymarket_*, pipeworx_*, ask_pipeworx*). Some clusters are internally consistent, but the blockchain endpoints break the verb convention entirely and there is no uniform pattern across the set.

Tool Count2/5

36 tools spanning at least six unrelated domains — blockchain explorer, structured-data research, prediction markets, AI visibility, memory, and subscriptions — is too heavy for a coherent server. The count exceeds the 25+ threshold and reflects scope creep rather than a focused purpose.

Completeness3/5

The Pipeworx research surface is near-complete (discover/resolve/ask/ground/verify/search-within plus entity/profile/compare), prediction markets are exhaustively covered, and memory/subscriptions have full lifecycles. But the server's namesake domain — Blockchair blockchain data — is thin at just five basic queries with no fee estimation, mempool, or deeper chain analytics, leaving notable gaps in the surface implied by the server name.

Resources