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Hive History

mcp_m7_hive_history

Browse saved Hive analysis reports and load full report content into the conversation to reference past analyses in follow-up discussions. Use list to find reports by topic, then load a filename.

Instructions

Browse and load past Hive analysis reports.

Use "list" to see all saved reports with dates, topics, and sizes. Use "load" with a filename to bring a full report into the conversation context.

Reports are saved automatically when running mcp_m7_hive_coordinator. Use this tool to reference past analyses in follow-up discussions.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of reports to list (default: 20)
queryNoOptional search query to filter reports by topic (for "list" action)
actionYesAction: "list" shows all saved Hive reports, "load" returns the full content of a specific report
filenameNoFilename to load (required for "load" action). Use "list" first to see available files.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.18.1

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses data provenance (reports are saved automatically by mcp_m7_hive_coordinator) and the notable side effect that 'load' injects full report content into the conversation context, which has real cost implications. It stops short of stating error behavior for missing filenames or whether reports are project-scoped, so not quite 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the capability, then immediately broken into per-action lines, then one line of lifecycle context. No filler sentences; every line carries an actionable point.

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?

There is no output schema, so the description must characterize returns — and it does, specifying that list shows dates, topics, and sizes while load returns full report content. Combined with the automatic-save provenance note, an agent has everything needed to call this correctly.

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 schema already documents limit, query, action, and filename, setting a baseline of 3. The description adds operational meaning on top by tying each action to its parameter (load needs a filename, list is the discovery step first), which is genuine value beyond the schema's field-level text.

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 first sentence states a specific verb pair (browse/load) and resource (past Hive analysis reports). The description also names the producing sibling, mcp_m7_hive_coordinator, so an agent can separate this read-back tool from the tool that generates the reports.

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 explicitly enumerates the two actions and the condition for each: 'list' to see saved reports, 'load' with a filename to bring a report into context. It also frames the real use case — referencing past analyses in follow-up discussions — which tells the agent when this tool is the right choice versus re-running analysis.

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