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Glama

Housing Intel

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already indicate idempotentHint=true and destructiveHint=false. The description adds valuable context about scoping by user identifier, persistence duration (24 hours for anonymous, permanent for authenticated), and the key-value pair storage model.

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 concise yet comprehensive, with no unnecessary words. It front-loads the core purpose and then adds details in a logical order.

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?

For a simple key-value memory tool, the description covers purpose, usage context, persistence behavior, and sibling relationships. No output schema is needed for such a straightforward tool.

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 descriptions cover both parameters (key and value), so baseline is 3. The description adds practical usage examples ('resolved ticker, target address, user preference, research subject') that clarify typical use cases 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 saves data for reuse, using specific verbs like 'save' and 'store', and distinguishes from siblings by mentioning 'recall' and 'forget'. It covers both conversation and session persistence.

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 tells when to use the tool ('when you discover something worth carrying forward') and how it pairs with 'recall' and 'forget'. Provides concrete examples of when it's appropriate.

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

A3.6/5.0
Disambiguation2/5

Several tools intentionally overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant explicitly matching stable behavior right now. entity_profille/recent_changes/compare_entities and the multiple polymarket scanning tools also cover closely related jobs, so an agent must read carefully to avoid picking the wrong variant.

Naming Consistency3/5

All tools use lowercase snake_case, which is a consistent base style. However, the naming grammar is mixed: proper verb_noun tools like compare_entities and validate_claim sit beside noun-phrase/domain tools like housing_market_screen and polymarket_edges, plus the awkward compound case_shiller_metro_compare. The housing_ and polymarket_ prefixes help, but the pattern is not uniform enough for a 5.

Tool Count2/5

41 tools far exceeds the 25+ threshold and the typical well-scoped 3-15 range. Many tools pertyain to Polymarket, npm scanning, llms.txt generation, and memory, which have little to do with Housing Intel, so the count is not earned by the server's stated domain.

Completeness4/5

For the housing domain specifically, the coverage is strong: market snapshot, affordability, employment, mortgage history, rental/property analysis, metro demand, signal scanning, and Case-Shiller comparisons cover the main data needs. The generic ask_pipeworx and deep_research tools also backfill specialized queries. The weakness is scope blur, not obvious missing housing operations.

Resources