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angrysky56

Advanced Reasoning MCP Server

query_reasoning_memory

Search memory to retrieve related insights, hypotheses, and evidence from previous reasoning sessions, helping build on established connections and solutions.

Instructions

Query the integrated memory system to find related insights, hypotheses, and evidence.

Useful for:

  • Finding similar problems solved before

  • Retrieving relevant hypotheses and evidence

  • Understanding connections between ideas

  • Building on previous reasoning sessions

Parameters:

  • session_id: The reasoning session to query within (required)

  • query: What to search for in memory (required)

Returns related memories with confidence scores and connection information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesReasoning session identifier
queryYesWhat to search for in memory
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (query memory system) and what it returns (related memories with confidence scores and connection information), which covers basic behavioral traits. However, it doesn't disclose important operational details like whether this is a read-only operation, potential rate limits, authentication requirements, or how confidence scores are calculated. The description adds value but leaves significant behavioral gaps.

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?

The description is well-structured and appropriately sized. It begins with a clear purpose statement, follows with a bulleted 'Useful for' section that efficiently communicates use cases, includes a parameters section, and ends with return information. Every sentence earns its place, and the information is front-loaded with the most important details first. No wasted words or redundancy.

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?

Given the tool's complexity (querying a reasoning memory system with 2 parameters), no annotations, and no output schema, the description provides adequate but incomplete coverage. It explains what the tool does and when to use it, but lacks details about the memory system's structure, how results are ranked, what 'connection information' entails, or error conditions. The description is complete enough for basic understanding but leaves important contextual gaps.

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?

The schema description coverage is 100%, with both parameters well-documented in the input schema. The description adds a 'Parameters' section that restates what's already in the schema without providing additional semantic context. It doesn't explain what constitutes a valid session_id format, provide query examples, or offer guidance on query construction. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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 clearly states the tool's purpose as querying an integrated memory system to find related insights, hypotheses, and evidence. It uses specific verbs ('query', 'find', 'retrieve', 'understand', 'build') and identifies the resource ('integrated memory system'). However, it doesn't explicitly differentiate this tool from sibling tools like 'search_system_json' or 'get_system_json', which appear to have similar search/retrieval functions.

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?

The 'Useful for' section provides clear context about when to use this tool (finding similar problems, retrieving hypotheses/evidence, understanding connections, building on previous sessions). This gives strong guidance on appropriate use cases. However, it doesn't explicitly state when NOT to use this tool or mention alternatives among the sibling tools, which would be needed for a perfect score.

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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