Holographic Memory MCP

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Read-onlyIdempotent

Find stored memories by natural-language query with metadata filters, tags, and ranking controls; returns ranked text previews for fuller retrieval.

Instructions

Find memories with metadata filters or ranking controls; use recall for simpler full-text answers. Read-only. Results contain text previews and relative ranking scores, not probabilities; use get to read a result in full. Semantic scores require a configured model.

Input Schema

Name	Required	Description
`k`	No	Maximum number of ranked previews to return.
`tags`	No	Labels; filters match memories carrying every listed tag.
`query`	Yes	Natural-language phrase to match against stored memories; nonblank, at most 4,096 UTF-8 bytes.
`filter`	No	Exact-match filter on top-level metadata keys.
`fusion`	No	How to combine lexical and semantic ranks: weighted score blend or reciprocal rank fusion.
`min_score`	No	Drop hits below this relative rank score (0 to 1); this is not a probability.
`lexical_weight`	No	Weight for lexical BM25 ranking before fusion, from 0 to 100.
`semantic_weight`	No	Weight for model-based semantic ranking before fusion, from 0 to 100; requires a configured model.

Output Schema

Name	Required	Description	Default
`count`	Yes	Number of returned hits.
`results`	Yes	Ranked hits with text previews and score details.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Oct 26, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, so the bare 'Read-only' sentence earns no credit. The description does add real behavioral context beyond the annotations: results are previews with relative ranking scores that are not probabilities, and semantic scoring requires a configured model. These are useful operational caveats, though the preview/pagination details are partly redundant with the output schema.

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 compact sentences with zero filler, front-loading the core purpose before alternatives and output caveats. Every clause carries a distinct instruction.

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 8 parameters, rich annotations, and an output schema, an agent has everything needed: purpose, sibling routing, and the key caveat that scores are relative not probabilistic and that semantic ranking requires a model. Nothing essential is missing.

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%, so every parameter including `k`, `tags`, `fusion`, `min_score`, and the weights is already documented. The description adds only the semantic-weight model requirement, which the schema also states; otherwise it does not add syntax or fusion guidance beyond structured fields, so baseline 3 applies.

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 opens with a specific verb and resource ('Find memories with metadata filters or ranking controls') and explicitly contrasts itself with `recall` for simpler full-text answers and `get` for full reads. An agent can distinguish this tool from its siblings without opening any schema.

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?

It names both alternatives and the conditions that select them: `recall` when you want simple full-text answers, `get` when you need a result in full. This is explicit routing, not implied usage.

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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