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Glama

Crawlbase

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?

Beyond the readOnly/idempotent annotations, the description discloses fan-out across SEC EDGAR, XBRL, USPTO, USAspending, FDA, DOL, GDELT/GNews, and GLEIF; the USPTO soft-fail until reactivation; GDELT→GNews fallback; and the meaning of sources_used/sources_failed. It also explains SEC EDGAR name resolution and private-company behavior, which is exactly the behavioral context an agent needs.

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 dense but front-loaded with trigger examples and the ALWAYS PREFER rule, and every output section earns its place given the lack of an output schema. It is slightly heavy as a single paragraph, but the level of detail is warranted for a tool with this many return sections and edge cases.

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 must convey the return contract, and it does thoroughly: recent_filings cap and URIs, fundamentals fields and sorting, patent sunset behavior, expected-empty FDA cases, hiring, news fallback, LEI, and sources_used/sources_failed. It also covers input resolution and failure modes, so an agent has enough context to invoke it 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?

The schema already documents type and value fully, so the baseline is 3. The description adds meaningful behavioral semantics beyond the schema: names resolve via SEC EDGAR, zero-padded CIKs are accepted, type values are interchangeable, and private companies return resolved:false with an explicit notes line. This enrichment justifies a score above baseline.

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 naturalistic trigger examples and states the exact function: a 'full cross-source profile of a US public company in ONE parallel call.' It names a specific action (profile/research/brief) and a specific resource (US public company), and clearly distinguishes itself from chaining single-pack SEC/XBRL/news lookups.

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?

It gives explicit trigger phrases ('Tell me about X', 'research Acme', 'brief me on Tesla') and a hard preference rule: 'ALWAYS PREFER over chaining single-pack... when the user asks for a holistic view.' It also tells the agent what to expect for private companies, no-data sections, and source failures, which is strong when-to-use/when-to-expect guidance.

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

A4/5.0
Disambiguation4/5

The toolset is largely distinct: scraping, research, prediction-market, memory, and subscription tools each have clear boundaries. The ask_pipeworx family and the six Polymarket tools are closely related variants, but their descriptions provide explicit usage guidance, so an agent can select correctly with attention.

Naming Consistency3/5

Most tools use snake_case with descriptive names, but conventions are mixed: brand-prefixed noun phrases (crawlbase_scrape, polymarket_arbitrage, pipeworx_trending) sit alongside verb_noun tools (compare_entities, validate_claim) and bare verbs (remember, subscribe). The result is readable but not predictable.

Tool Count2/5

At 34 tools, the server spans several distinct domains (web scraping, structured data research, prediction markets, memory, subscriptions, feedback), making it feel like a kitchen sink rather than a focused toolset. The count is beyond the 'heavy' threshold and would benefit from splitting into separate servers.

Completeness4/5

The research surface is thorough: routing, grounded answers, deep research, entity profiles, comparisons, claim validation, and identifier resolution cover most real-world data needs. Minor gaps exist, such as no explicit tool to fetch pipeworx:// resource URIs and no crawler management for the scraping side.

Resources