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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, news, GLEIF and returns: cik + company_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); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today; person/place coming soon.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds contextual details: fans across multiple sources, specifics of return fields, patent sunset soft-fail, and fallback mechanisms. Further detail on rate limits or error handling would push to a 5.

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 relatively long but front-loaded with examples and a clear one-liner. Each sentence contributes value (sources, return fields, limitations). Could be slightly more concise without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers multiple data sources, return fields, patent sunset, and fallback. No output schema, but description details return structure adequately. Could mention error handling or rate limits for completeness.

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 coverage is 100%, so the description doesn't need to add much. It does clarify that 'value' accepts ticker or zero-padded CIK and that 'names not supported', which aligns with schema. No additional semantics beyond schema, so 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 clearly states 'full cross-source profile of a US public company in ONE parallel call' and provides example queries like 'tell me about X' and 'company profile for Microsoft', making the purpose specific and distinct from sibling tools.

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?

Explicitly instructs to 'ALWAYS PREFER over chaining single-pack lookups when holistic view is needed' and warns that names are not supported, directing to use 'resolve_entity' first. This provides clear when-to-use and when-not-to-use 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

B3.4/5.0
Disambiguation2/5

The tool set has several near-duplicate entries (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; five polymarket_* tools), and the server name implies topography while most tools serve unrelated data lookups, making it hard to select the right tool for a task.

Naming Consistency3/5

Most tool names use snake_case and a verb-first style, but there are notable exceptions like 'datasets', 'dem', and 'forget', and the 'pipeworx' prefix is applied inconsistently (pipeworx_feedback, pipeworx_trending vs. ask_pipeworx). The pattern is readable but not fully uniform.

Tool Count2/5

With 34 tools, the count is excessive for a server named Opentopography, especially since only 3 tools (datasets, dem, point_elevation) relate to the implied domain. The bulk of tools belong to a general-purpose data and prediction-market service, creating a severe scope mismatch.

Completeness2/5

For the implied topography domain, the surface is severely incomplete: only dataset listing, a raster fetch, and a point elevation lookup are present, missing expected operations like elevation profiles, point cloud access, or data processing. For the broader Pipeworx domain, coverage is broad but this does not match the server's stated focus.