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memanto-mcp
by geoffsdesk

memanto_answer

Synthesizes RAG-grounded, cited answers from an agent's stored memories, retrieving relevant context instead of returning raw search hits.

Instructions

RAG-grounded answer using the agent's memory.

Memanto retrieves relevant memories and synthesizes an answer with citations. Use this when the user wants a synthesized response, not raw search hits.

Input Schema

NameRequiredDescriptionDefault
queryYes
agent_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations supplied, the description carries the full behavioral burden and does disclose the core pipeline: retrieval over the agent's memory followed by synthesis with citations. It does not state what happens when no relevant memories exist, whether the call is strictly read-only, latency/cost implications, or how agent_id scopes the retrieval corpus, all of which matter for a synthesis tool.

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?

Three short lines, front-loaded with the purpose and then the usage rule; no filler sentences. The second and third lines partially restate the synthesis idea, so it is tight but not maximally economical.

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 values need not be explained, and the purpose plus usage rule are present. However, for a multi-stage RAG tool with zero annotation coverage and two undocumented required parameters, the definition leaves the agent guessing about scoping and empty-result behavior.

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

Parameters2/5

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

Both parameters are required with 0% schema description coverage, so the description must compensate and largely does not. 'query' is self-evident from the wording, but 'agent_id' is never explained — the phrase 'the agent's memory' only hints that it selects the memory scope, without saying whether it must be an existing agent or what happens on an unknown id.

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?

The description states a specific verb+resource combination: it retrieves memories and synthesizes 'a RAG-grounded answer with citations.' The phrase 'not raw search hits' signals it is distinct from a plain retrieval sibling such as memanto_recall, but that sibling is never named, so the differentiation is implicit rather than explicit.

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?

It gives a clear selection rule: 'Use this when the user wants a synthesized response, not raw search hits,' which covers both when to use it and when not to. The gap is that it never points to the concrete alternative (memanto_recall / memanto_recall_current) an agent should pick for raw hits, so the routing is one-sided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.