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Glama

Landprice

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)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations indicate idempotentHint=true, and the description adds behavioral details: persistence (authenticated vs anonymous 24-hour lifetime), scoping by identifier. No contradiction with annotations.

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 (4 sentences), front-loaded with purpose, and structured logically. Every sentence adds necessary context without 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?

For a simple key-value store tool with no output schema, the description covers all essential aspects: purpose, usage guidance, behavioral traits (persistence, lifetime, scoping), and parameter semantics.

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% with parameter descriptions. The description's parameter examples ('subject_property', 'target_ticker') add some value, but overall the schema already defines parameter meaning adequately.

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: 'Save data the agent will need to reuse later'. It uses a specific verb (save) and resource (data), and distinguishes from siblings like recall and forget.

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?

The description provides explicit usage context: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also mentions pairing with recall and forget, though does not explicitly state when not to use it.

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

All Landprice toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries. `ask_pipeworx_beta` is explicitly described as currently identical to `ask_pipeworx`, `discover_tools` overlaps with `suggest_questions`, and the six Polymarket tools (`bet_research`, `polymarket_edges`, `polymarket_arbitrage`, etc.) blur together for opportunity-finding. An agent would struggle to pick the right tool without reading every description carefully.

Naming Consistency4/5

Naming is overwhelmingly consistent snake_case with a verb_noun or noun pattern (`ask_pipeworx`, `list_subscriptions`, `validate_claim`, `recent_changes`). The `polymarket_*` and `ask_pipeworx_*` families follow clear conventions. Minor deviations like `bet_research`, `entity_profile`, and `landprice_points` being noun-first are still readable and predictable.

Tool Count2/5

32 tools is heavy for a server named 'Landprice' when exactly one tool (`landprice_points`) actually concerns land prices. The vast majority of tools constitute an unrelated general-purpose data research and prediction-market platform, making the count feel bloated and scattershot relative to the server's stated purpose.

Completeness3/5

For the actual broad scope revealed by the tools — structured data lookup, grounded verification, deep research, entity resolution, comparison, monitoring, and memory — the surface is reasonably complete with no obvious dead ends. However, for the 'Landprice' domain implied by the server name, coverage is nearly absent: only Japan is covered, with no other countries, address search, or property-level data.

Resources