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iai-mcp

memory_recall_structural

Read-onlyIdempotent

Retrieve memory content using role-filler structural queries, e.g., find records where agent matches a specific name. Reads from BSC hypervector bindings without modification.

Instructions

Structural recall via TEM role->filler bindings (BSC hypervectors). Read-only. Prefer over memory_recall for role-filler queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
structure_queryNoOptional role->filler map, e.g. {"agent": "agent_name"}. Each value is hashed to a filler hypervector. When omitted or empty, query HV is zero-filled and every row with structure_hv is scored (expensive at large N).
budget_tokensNoSoft token budget for the response (default 2000). Hits are appended until the next would exceed this budget.
max_recordsNoHard cap on records scanned after fetch (default 5000, max 50000). Prevents accidental full-corpus scans from `{}`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsNo
anti_hitsNo
activation_traceNo
budget_usedNo
structural_query_sizeNo
Behavior5/5

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

Adds significant behavioral details beyond annotations: explains the hypervector mechanism, the effect of empty query (zero-filled, expensive), and how budget_tokens and max_records limit operation. 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?

Extremely concise: one sentence covers purpose, usage, and key behavioral notes. No redundant information. Information density is high without being verbose.

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?

Given the presence of output schema, full annotation coverage, and high schema description coverage, the description is fully adequate. It explains the tool's unique role, usage context, parameter behaviors, and safety mechanisms.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 100% schema coverage, description adds crucial semantics: clarifies that structure_query optional map triggers row scoring differently when empty, explains budget_tokens as soft token budget, and max_records as hard cap to prevent accidental full scans.

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?

Clearly states 'Structural recall' using role-filler bindings, distinguishing from 'memory_recall' by specifying the query type. The verb 'recall' indicates retrieval, and the resource is memory via TEM structures.

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 says 'Prefer over memory_recall for role-filler queries', giving clear when-to-use guidance. Also warns that empty structure_query is expensive, implying when to avoid.

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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