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Glama

Realestateapi

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds that returned tools are 'ready to call directly, no second schema lookup needed,' which is valuable behavioral context beyond the annotations themselves.

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?

Single paragraph, front-loaded with main purpose, every sentence provides distinct info. No 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?

Even without output schema, the description fully explains what the tool returns (top-N tools with names, descriptions, schemas, examples). For a discovery tool, this is complete and actionable.

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 by mentioning aliases for query parameter and providing concrete examples (e.g., 'analyze housing market trends'), though it does not deeply explain parameter usage beyond the schema.

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 the tool's purpose: finding tools by describing the data or task. It lists many specific domains (SEC filings, financials, FDA drugs, etc.) and explains that it returns top-N relevant tools with full schemas, distinguishing it from specific task tools.

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?

Provides explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set.' While it doesn't explicitly state when not to use it, the context implies it's for discovery rather than executing a specific task.

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