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Arkiv MCP Server

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query_entities

Retrieve entities from an Arkiv chain using SQL-like filter expressions (comparisons, logical operators, glob, $owner, $creator, $key). Paginate with cursor, limit results, and select included fields.

Instructions

Query entities from an Arkiv database chain using a filter expression. The filter uses a SQL-like syntax: comparisons (=, !=, <, >, <=, >=), logical operators (&& and ||), negation (!), and glob matching (~). Special attributes: $all (match everything), $owner (current owner address), $creator (original creator address, immutable), $key (entity key). Examples: '$all', 'status = "active"', 'score > 100', 'project = "myapp" && status = "active"', '$owner = "0x1234..."', 'name ~ "test*"'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoFilter expression. Defaults to "$all" if omitted. Examples: 'status = "active"', 'score > 100 && type = "post"', '$owner = "0xAbc..."', '$creator = "0xDef..."', 'name ~ "prefix*"'
limitNoMaximum number of entities to return (max 200)
cursorNoPagination cursor returned by a previous query
withAttributesNoInclude attributes in results (default: true)
withMetadataNoInclude metadata (owner, creator, expiry) in results (default: true)
withPayloadNoInclude payload in results as base64 (default: true)
validAtBlockNoQuery state at a specific block number
Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains the filter syntax and special attributes but does not disclose performance implications, rate limits, or confirm that it is read-only. Schema covers some aspects (e.g., limit), but behavioral context is lacking.

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 a single, well-structured paragraph that front-loads the core purpose, then details the filter syntax, special attributes, and examples. Every sentence adds value without redundancy.

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 no output schema, the description does not explain return values explicitly, but the tool name and parameters imply entities with requested fields. Pagination cursor is mentioned in schema but not explained in description. Overall, it is fairly complete for a query 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 description coverage is 100%, so baseline is 3. The description adds meaningful context for the 'filter' parameter by detailing the syntax and special attributes ($all, $owner, etc.), which goes beyond the schema. Other parameters are not enhanced, but the main parameter benefits significantly.

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 that the tool queries entities from an Arkiv database chain using a filter expression, with specific SQL-like syntax and special attributes. It is a specific verb-resource pair, and there are no sibling tools to distinguish against.

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 examples and syntax details that imply when to use the tool, but does not explicitly state when not to use or mention alternatives. Since there are no sibling tools, the lack of exclusion is less critical.

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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