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knowmind

by Schubeler-Consulting
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JavaScript
Hybrid

knowmind_recall

Read-onlyIdempotent

Retrieve relevant knowledge from past conversations and documents to answer questions and support decisions. Access prior context when needed to ensure accurate, informed responses.

Instructions

Retrieve relevant knowledge before answering anything that benefits from prior context - decisions, preferences, project state, people, deadlines, past conversations. Hybrid recall (BM25 + pgvector + graph hops) over the tenant corpus; returns the top-k memory chunks, each with a relevance score and source reference. Suitable whenever earlier context would improve the answer; use knowmind_recall_at_time when you need what was valid at a past point in time. Read scope suffices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoNumber of chunks to return (1-25, default 5).
hopsNoGraph expansion depth over typed relations (0-3, default 2); 0 = text/vector only, no graph.
queryYesNatural-language question; full sentences retrieve better than keywords.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.7

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover read-only and non-destructive hints; the description adds meaningful behavioral detail: hybrid recall (BM25 + pgvector + graph hops), top-k chunks with relevance scores and source references, and 'Read scope suffices' for permissions. No contradictions.

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 well-structured and front-loaded, but contains minor redundancy: 'Retrieve relevant knowledge before answering anything...' and 'Suitable whenever earlier context would improve the answer' overlap. Overall it is appropriately sized, earning a strong score.

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 tool's simplicity, the description covers the operation, when to use, return format (top-k chunks with score and source), and safety. The annotation set plus description provide a complete picture; no output schema exists, but the description compensates for it.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining the hybrid recall approach and the role of graph hops, which gives meaning to k and hops parameters, though it does not detail each parameter's syntax.

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 uses a specific verb ('Retrieve relevant knowledge') and resource (memory chunks from the tenant corpus), and explicitly distinguishes itself from knowmind_recall_at_time. It leaves no doubt about what the tool does.

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?

The description explicitly states when it is suitable ('whenever earlier context would improve the answer') and names the alternative (knowmind_recall_at_time) for past-point-in-time retrieval. This is clear when/when-not guidance.

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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