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Penfield Memory

by io.github.penfieldlabs
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[ChatGPT Connector compat] Search for memories.

Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool
contract. Native MCP clients (Claude Desktop, Claude Code, Perplexity)
should prefer `recall`, which exposes richer filters (tags, date range,
source_type) and identical hybrid-search semantics under the hood.

Returns results with citation support (id, title, url, text fields).

Args:
    query: Search query
    limit: Maximum results (default 10)
    include_inactive: Include superseded/contradicted memories (default: false)
    ctx: MCP context

Returns:
    Dict with 'results' array containing id, title, url, text fields

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
include_inactiveNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / include_inactive
      Added value: +{
+  "default": false,
+  "title": "Include Inactive",
+  "type": "boolean"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that the tool returns results with citation support (id, title, url, text) and shares identical hybrid-search semantics with recall. No contradictions, and it adds value beyond annotations by detailing return fields.

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 front-loaded with purpose, then explains context, then return format, then parameters. While not overly long, it includes necessary explanations. Could be slightly more concise, but the structure is logical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and presence of output schema and annotations, the description covers purpose, usage guidance, return fields, and parameters. It lacks details on pagination or default behavior when limit is exceeded, but overall provides sufficient context for an AI agent.

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?

Schema description coverage is 0%, so the description should add meaning. However, it mostly restates schema defaults and titles for `query` and `limit`. For `include_inactive`, it adds 'superseded/contradicted memories', which is helpful. The description also lists a `ctx` parameter not present in the schema, causing confusion. Overall minimal added value.

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 clearly states the tool searches for memories. It distinguishes from sibling `recall` by noting `recall` is preferred for native MCP clients, and explains this tool exists for ChatGPT Connector compat. The verb 'search' and resource 'memories' are specific.

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 says when to use this tool: for ChatGPT Connector compat, and advises native MCP clients to prefer `recall`. This provides clear usage context and direct alternatives.

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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