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Glama

Realestateapi

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds rich behavioral context: parallel fan-out across multiple sources, source-level success/failure reporting, empty sections meaning genuine no-data rather than bugs, the patents API sunset soft-fail, and the FDA biologics nuance. No annotation contradiction exists.

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 front-loaded with purpose and example triggers, and nearly every detail is functional. It is long and somewhat stream-of-consciousness, with minor repetition at the end ('both take the same `value` shapes above'), which prevents a perfect score, but no sentence is filler.

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?

There is no output schema, so the description carries the burden of explaining return values; it does so well, covering core fields, source-specific caveats, fallback behaior, and failure semantics. Given the tool's complexity, an agent has enough to select and invoke it correctly and to interpret unexpected results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description goes beyond the schema by giving concrete examples ('AAPL', '0000320193', 'Moderna'), explaining name resolution via SEC EDGAR, clarifying that `type` accepts company and ticker interchangeably, and describing the resolved_from/resolved_to behavior. This materially helps an agent choose correct parameter values.

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?

Description opens with concrete example queries and the precise job: 'full cross-source profile of a US public company in ONE parallel call.' It names the exact output areas (CIK, filings, fundamentals, patents, contracts, FDA products, hiring, news, LEI) and implicitly separates itself from single-focus siblings like resolve_entity or compare_entities by emphasizing a single holistic profile call.

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?

Explicit routing rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also tells the agent what inputs are accepted (ticker, zero-padded CIK, or company name) and what happens for private companies, so the agent knows when this tool is appropriate and what alternative behavior to expect.

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

All Realestateapi toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research and ask_pipeworx both answer broad factual questions, and the polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) cover heavily overlapping edge/arb research territory. An agent could easily route a query to the wrong one.

Naming Consistency2/5

Most tools use snake_case, but there is no consistent verb_noun pattern: ask_pipeworx, deep_research, bet_research, recent_changes, remember/recall/forget, generate_llms_txt, realestateapi_property_detail, and polymarket_edges all follow different structural conventions. The server name Realestateapi also does not match the broader Pipeworx/polymarket tool set.

Tool Count2/5

34 tools is above the 25+ threshold for a heavy, hard-to-navigate surface, especially for a server named Realestateapi where only 3 tools actually concern real estate. Many tools are generic utilities, memory helpers, feedback channels, and prediction-market tooling that feel unrelated to the apparent real-estate API scope.

Completeness2/5

For a real-estate-focused server, the surface is significantly incomplete: property search, property detail, and skip-trace cover only basic owner/value lookups. Missing obvious real-estate capabilities like comparable sales, tax history, market trends, school/flood data, and listing lifecycle operations create notable gaps an agent would need to work around.

Resources