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Read-onlyIdempotent

Fetch observations before and after an anchor ID or search query to view local chronological context. Set depth_before and depth_after to control how many entries appear around the center point.

Instructions

Read-only. Return a JSON array of observations before and after one center point; provide anchor or query, and anchor takes precedence when both are set. depth_before and depth_after default to 5, and project limits both anchor lookup and results. Use this for local chronological context around an observation; use timeline_report for an aggregated project report, search for curated memories, or current_state for a stable fact. Missing anchors/queries, no query match, or database failures return a tool error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoSearch query to find anchor
anchorNoAnchor observation ID
projectNoProject name filter
depth_afterNoObservations after anchor (default 5)
depth_beforeNoObservations before anchor (default 5)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
observationsYes
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description reveals important behavioral traits: anchor takes precedence over query, depth_before/after default to 5, project limits both anchor lookup and results, and error conditions (missing anchors/queries, no match, DB failures). This is substantial context beyond structured annotations.

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 dense but efficient: four sentences conveying read-only behavior, return type, conditional anchor/query, defaults, project scope, sibling alternatives, and error handling. Every sentence carries critical information with no fluff.

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?

Given the output schema exists and parameters are optional, the description covers all key aspects: purpose, input semantics, defaults, error behavior, and alternatives. It leaves no significant gaps for the agent to misuse the tool.

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?

The schema covers all 5 parameters with descriptions (100% coverage), but the description adds relational semantics not present in the schema, such as anchor/query precedence and the effect of project on both lookup and results. This helps the agent understand how parameters interact, exceeding the baseline for fully documented schemas.

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 'Return a JSON array of observations before and after one center point' with a specific verb and resource, and it explicitly distinguishes this tool from siblings such as timeline_report, search, and current_state by naming alternatives.

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 provides explicit usage guidance: 'Use this for local chronological context around an observation' and contrasts with timeline_report, search, and current_state. It also warns that missing anchors/queries, no match, or database failures return a tool error, which helps set expectations for correct usage.

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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