Skip to main content
Glama

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.

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description reveals parallel fan-out, GDELT→GNews fallback, sources_used/sources_failed semantics, empty-section meaning, name resolution, and resolved:false+notes for private companies. This is far more transparency than the annotations alone provide.

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 front-loaded with the core purpose and usage preference, then enumerates sources, return fields, and edge cases in an organized flow. A few examples are redundant with the schema's type/value descriptions, preventing 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?

With no output schema, the description carries the burden of explaining return values, and it does so by listing every major section, the source behind it, fallback behavior, and edge-case responses. The level of detail is sufficient for an agent to call and interpret the result 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, but the description adds examples, zero-padded CIK formatting, SEC EDGAR name resolution, and explicit interchangeability of type values. It reinforces the schema rather than replacing it, so 4 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 immediately frames the tool as a 'full cross-source profile of a US public company in ONE parallel call' and anchors it with concrete user phrasings. It is specific about the resource, scope, and source breadth, and explicitly contrasts with chained single-source lookups.

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 over chaining single-pack SEC/XBRL/news lookups for holistic profile requests, and gives accepted input shapes. It does not explicitly contrast with sibling tools like deep_research or compare_entities, which limits it slightly below a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx and ask_pipeworx_beta are functionally identical today, and the polymarket_edges/arbitrage/fill_risk/kalshi_spread family plus entity_profile/recent_changes/compare_entities cover adjacent jobs. The descriptions are detailed enough to separate them with careful reading, but an agent could easily select the wrong one without deep inspection.

Naming Consistency3/5

The set has recognizable prefixes like ecos_, ask_pipeworx, and polymarket_, but it also mixes verb_noun names (validate_claim, discover_tools), bare verbs (remember, forget, recall), reversed/gerund forms (bet_research, pipeworx_trending), and special tokens (generate_llms_txt). The naming is readable on a per-family basis but not predictable across the full surface.

Tool Count2/5

35 tools is above the comfortable range for a coherent tool set, and several entries are near-duplicates or wrappers: ask_pipeworx_beta is currently identical to ask_pipeworx, and scan_competitor_ai_presence wraps ai_visibility_check. The prediction-market and company-research families could be consolidated without losing capability.

Completeness4/5

For the server's broad scope, lifecycle coverage is strong: ECOS has search/items/get/indicators, subscriptions have create/list/read/cancel, memory has save/read/delete, and the data-research surface covers lookup, grounded verification, comparison, profiles, changes, and discovery. Minor gaps exist, such as no direct tool to fetch a pipeworx:// record by URI or execute a single catalog tool directly, but these are workable.