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ENYAL by Greenland AI

query_memory_content

Read-onlyIdempotent

Execute a structured query plan and return DECRYPTED memory CONTENT (node names, summaries, properties; edge evidence + endpoint names) alongside structure. SAME plan schema as query_memory. REQUIRES a memory:read:content-scoped token — a structure-only token is rejected (403) by ENYAL. Account scope is enforced server-side; it cannot be set from the plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planYesThe structured query plan (a JSON object): entity_filters [{node_type} | {name_hash}], edge_filters [{relationship, direction out|in|both}], temporal_bounds {valid_from_after, valid_to_before} (ISO 8601), limit (<= 200), include_neighbours_depth (0-5). The schema and the allowed node_type / relationship values are in this tool's description.
include_chunksNoAlso return the chunk ids behind each node. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

On top of annotations that already cover the safety profile (readOnly, idempotent, non-destructive), it discloses concrete behavioral facts: a memory:read:content-scoped token is required, a structure-only token is rejected with 403 by ENYAL, and account scope is enforced server-side and cannot be overridden from the plan. These are exactly the operational details an agent needs and that annotations cannot express.

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?

Three tight sentences, front-loaded with the primary action and return content, followed by the schema relationship and the auth constraint. No filler; every sentence carries distinct information.

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?

With no output schema, the description compensates by naming what is returned (node names, summaries, properties, edge evidence + endpoints), and it covers the key auth/scope constraints for a nested-plan tool. An agent has everything needed to invoke it correctly.

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%, and the plan parameter already carries a detailed inline description of its filters, bounds, and limit. The description adds no per-parameter detail and only notes the plan schema is shared with query_memory, so the baseline 3 for a well-documented schema is appropriate.

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?

States a specific verb (execute a structured query plan) and precisely what it returns: decrypted node names, summaries, properties, and edge evidence with endpoint names. It explicitly distinguishes itself from query_memory by noting the 'SAME plan schema' but decrypted-content behavior, so an agent can tell the two apart without opening a schema.

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?

Clearly signals the differentiator versus query_memory (decrypted content vs structure-only) and states the prerequisite token scope. It does not explicitly say 'use query_memory when you only need structure', so the when-not is implied rather than stated.

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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