Skip to main content
Glama

timeline

Trace how a topic changed over time, oldest first, grouped by day with facts and the passages where it was discussed.

Instructions

How a topic changed over time, oldest first, grouped by day: the facts about it in the order they became true, including those marked ĐÃ HẾT ĐÚNG (true once, replaced later), next to the passages where it was discussed. Use it for "how did we end up deciding X" or "what happened with Y over the last weeks".

Facts are picked by closeness in meaning, so a few may be only loosely related: ignore those. Each entry
carries its episode id and turn numbers for citing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of facts.
sinceNoOnly from this point: 2026-10-13 or 2026-10-13T09:30.
topicYesThe topic, decision or project, in plain words, e.g. "Redis" or "notification bell in the admin page". Do not put dates here; use since and until.
untilNoOnly before this point. A plain day counts the whole day.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden and does well: it states ordering (oldest first), day-based grouping, that superseded facts marked ĐÃ HẾT ĐÚNG are still included, that matching is semantic and may surface loosely related facts, and that each entry exposes episode id and turn numbers for citation. It stops short of describing size/pagination behavior beyond the limit parameter.

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 first sentence front-loads the ordering and grouping contract, and the second gives the routing cues. It is slightly dense and the parenthetical Vietnamese marker breaks flow, but no sentence is wasted.

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?

An output schema exists, so return-value documentation is not required, yet the description still explains the entry structure (episode id + turn numbers) and the noise caveat. Combined with a fully documented schema, an agent has what it needs to call this correctly; only the sibling boundary is unstated.

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?

Schema description coverage is 100% and all four parameters (topic, limit, since, until) are documented in the schema, including the guidance not to put dates in topic. The description adds no parameter semantics of its own, so the baseline 3 applies.

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 names a specific resource (a topic's evolution over time) and a specific shape (facts in chronological order, grouped by day, alongside commentary passages). That is far more than a restatement of the name, but it never distinguishes itself by name from the siblings search/summary/people, so an agent must infer the boundary.

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?

It supplies concrete triggering questions ('how did we end up deciding X', 'what happened with Y over the last weeks') that make the intended usage clear. It does not, however, say when NOT to use it or name search/summary as the alternative for a simple lookup, so routing is left partly to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools