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Glama

land_liquidity_pool_by_symbol

Get a Splinterlands liquidity pool by symbol (e.g., GRAIN or VOUCHER) to view its quantities, prices, and total shares.

Instructions

Get one liquidity-pool object by symbol, as GET /land/liquidity/poolsbysymbol/{symbol} returns it. All five tested casings GRAIN, grain, Grain, VOUCHER and voucher returned the correct matching pool; this records five observed working casings and is not a general rule about every possible input. An unknown symbol returned HTTP 200 with data:null. The object fields retain the mixed wire types observed on the list route: resource_quantity, dec_quantity and total_shares are JSON strings, while prices are JSON numbers. This server sends the symbol as supplied, returns the upstream response unchanged and does not manufacture a pool object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.2
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv0.0.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: it discloses the unknown-symbol HTTP 200 with data:null edge case, the mixed JSON wire types for fields, the pass-through behavior of the symbol, and the fact that the server does not fabricate a pool object.

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 verbose but each sentence carries operational information an agent would not otherwise know. It is front-loaded with the core purpose, and the supporting observations are grouped logically, though some could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter and lack of output schema, the description covers the critical edge cases, field type expectations, and upstream behavior. It does not lay out the full object shape, but enough is present for safe invocation and interpretation of the response.

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?

The schema only specifies a string with minLength 1, so the description adds real value by explaining that the symbol is passed through as supplied, that casing behavior is empirically observed for five values rather than guaranteed, and that unknown symbols produce a null result. It still does not enumerate valid symbol values beyond the examples.

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 clearly states a specific verb+resource: 'Get one liquidity-pool object by symbol' and anchors it to the exact upstream endpoint. This distinguishes it from sibling tools like the list route or land_liquidity_pool_by_id.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose implies when to use it (single pool by symbol), but there is no explicit guidance about when to prefer this over land_liquidity_pool_by_id or the list-based land_liquidity_pools. No alternatives or exclusions are named.

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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