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iai-mcp

memory_recall_structural

Read-onlyIdempotent

Query long-term memory by specifying role-filler bindings like {'agent': 'alice'} to retrieve relevant records. Each value is hashed to a hypervector for similarity scoring.

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": "alice"}. 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
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds 'Read-only' which is consistent but does not provide further behavioral context beyond annotations. No contradiction.

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 extremely concise, using only three short sentences that each add value: function, safety hint, usage guidance. No filler or 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 the well-detailed schema, annotations, and output schema, the description is mostly complete. It could mention performance implications of empty queries (though schema does), but overall it provides sufficient context for correct invocation.

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 detailed parameter descriptions (e.g., structure_query's behavior when omitted). The description adds minimal extra meaning about the underlying TEM/BSC mechanism, but the schema already carries the semantic load, so baseline 3 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?

The description clearly states it performs 'Structural recall via TEM role->filler bindings (BSC hypervectors)' and distinguishes from sibling 'memory_recall' by advising preference for role-filler queries, making the purpose specific and differentiated.

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?

Explicitly advises 'Prefer over memory_recall for role-filler queries', giving clear context for when to use this tool over its main alternative. However, it does not address when not to use it or compare to other siblings.

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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