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Longhand

Longhand

by Wynelson94
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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recall

Retrieve past sessions by asking natural language questions about projects, episodes, or fuzzy time references. Returns matched projects, problem-fix pairs, diffs, and file states in one call.

Instructions

PROACTIVE MEMORY — START HERE for any 'do you remember...' question. Handles fuzzy time references ('a couple months ago'), project matching ('that game project'), and episode retrieval in ONE call. Returns: matched projects, relevant episodes (problem→fix pairs), diffs, verbatim thinking blocks, reconstructed file states, and a prebuilt markdown narrative. Do NOT manually search and paginate — use this tool first.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "recall"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesNatural language question
max_charsNoMax total output characters
max_episodesNoMax episodes to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

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 details the return types: matched projects, episodes, diffs, thinking blocks, reconstructed files, and a narrative. It mentions 'in ONE call' but does not disclose authorization needs or rate limits, though the comprehensive output description covers most behavioral aspects.

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 concise, with four sentences each adding unique value: attention-getter, capabilities, specific returns, and usage instruction. It is well-structured and front-loaded with the most important information.

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?

The tool is complex with no output schema, but the description fully explains its purpose, input, output, and usage context. It covers why to use it over alternatives and what to expect, making it complete for an agent to select and invoke 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%, baseline 3. The description adds meaning by explaining that the query parameter handles natural language with fuzzy time references and project matching, enhancing understanding beyond the schema's 'Natural language question' description.

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 is for memory recall, handling fuzzy time references, project matching, and episode retrieval in one call. It distinguishes from siblings by explicitly advising not to manually search or paginate, making it the first tool for recall questions.

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 'START HERE for any 'do you remember...' question' and instructs 'Do NOT manually search and paginate — use this tool first,' providing clear when-to-use and when-not-to-use 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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