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palimpsest-labs

Unified History MCP

summary

Fetch an AI-written summary of a specific entry—such as a session log, transcript, or notification—by supplying its domain and ID. Skim the key points without reading the full content.

Instructions

Get the AI-generated summary for a domain entry.

Args: domain: Domain (sessions, transcripts) id: File/directory name or unique prefix

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
domainYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It only states 'Get', which implies a read operation, but does not explicitly confirm it is non-destructive or mention any side effects such as triggering summary generation if not already cached. No details about rate limits, permissions, or potential delays are provided.

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 minimal and front-loaded with the core purpose, followed by concise parameter explanations. There is no fluff; every sentence earns its place. The use of an Args block is clean and readable.

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?

The presence of an output schema covers return values, so the description need not explain them. For a simple getter tool with two parameters, the description is mostly complete. However, it lacks context about how 'summary' differs from 'read' or 'search', and does not address edge cases like missing summaries. This prevents a perfect score.

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 0%, so the description must compensate. It does so by defining 'domain' with example values ('sessions, transcripts') and clarifying 'id' as 'File/directory name or unique prefix.' This adds meaning beyond the bare schema and is genuinely helpful for correct invocation.

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's function: 'Get the AI-generated summary for a domain entry.' This is a specific verb+resource pair and differentiates it from siblings like 'read' (which likely retrieves raw content) and 'search' (which looks for entries). The resource ('AI-generated summary') and scope (domain entry) are unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention that this is the appropriate tool for retrieving summaries, nor does it exclude cases where 'read' or 'search' would be preferable. No contextual cues about preconditions or selection criteria are offered.

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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