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amu_search

Retrieve Analytical Memory Units by semantic similarity to a natural-language query, with results filtered by Postgres row-level security to only include units derived from permitted columns.

Instructions

Search lineage-gated Analytical Memory Units (AMUs) by semantic similarity to a natural-language query. Results are lineage-gated by Postgres row-level security under this server's own Postgres role: an Analytical Memory Unit (AMU) is only ever returned if every sensitive column touched by its derivation is in this role's permitted set. This tool cannot bypass that gate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
queryYes
metric_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden and does well on the security-relevant behavior: it explains that results are lineage-gated by Postgres row-level security under the server's role, that AMUs are only returned if every sensitive column is permitted, and that this tool cannot bypass the gate. It doesn't cover rate limits, empty-result behavior, or ordering/relevance thresholds, so it falls short of a 5.

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 front-loaded with the core action and constraint, then supplies the security caveat. It is dense but every sentence adds information; minor redundancy in explaining the gate twice ('lineage-gated' and the full RLS explanation) slightly inflates the length.

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

Completeness3/5

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

An output schema exists, so return format needn't be explained. However, with 0% schema description coverage and no parameter documentation, plus no usage guidance against siblings, the definition is incomplete for a tool that takes an undocumented k and an optional metric_name filter.

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

Parameters1/5

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

Schema description coverage is 0% and all three parameters are undocumented in both schema and description. Neither 'k' (result count?), 'metric_name' (filter?), nor 'query' (natural-language text) is explained, leaving the agent to guess the meaning and constraints of each parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Search), resource (AMUs), and mechanism (semantic similarity to a natural-language query). It is clearly distinguished from siblings amu_record (write) and amu_check_conflict, though it doesn't explicitly reference them. The lineage-gating detail focuses on return eligibility rather than the search action itself.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus siblings like amu_check_conflict, and no prerequisites or exclusions are stated. An agent must infer from the name that this is the read/retrieval counterpart, which is left implicit.

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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