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Glama

Realestateapi

by io.github.pipeworx-io

Scan Dependency

scan_dependency
Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Description fully aligns with annotations (readOnly, idempotent, not destructive). Adds context: fans out across external services, partial failures degrade gracefully with sources_failed reporting, and bundlephobia timing out is possible. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose. Each sentence provides essential information: composite nature, sources, return block, usage guidance, limitations, and partial failure handling. No waste or redundancy.

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 the complexity (composite of two external services), no output schema, and only 2 parameters, the description is remarkably complete. It details the summary block fields, per-advisory details, links, alternative versions, and degradation behavior. No obvious gaps.

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 baseline is 3. The description adds value beyond schema: confirms scoped packages accepted for 'package' and clarifies default behavior for 'version' (defaults to latest). This extra guidance justifies a score of 4.

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 identifies it as a composite check for npm packages that combines deps.dev and bundlephobia data. It uses specific verbs ('scan', 'check') and resource ('dependency'), and effectively distinguishes from sibling tools by emphasizing it's a single-call composite, unlike individual API queries.

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 states when to use it: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Provides exclusion criteria: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'. Also mentions potential delays and graceful degradation.

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