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AutomateLab-tech

Citation Intelligence MCP

track_queries

Save, load, or list named query panels to monitor specific queries over time. Create a panel with queries, then recall or enumerate panels for ongoing tracking.

Instructions

Save, load, or list named query panels. A panel is a persisted set of queries you want to monitor over time (e.g. editorial-watchlist). Use action=save with queries[] to create, action=load to read, action=list to enumerate. Panels live under /panels/.json.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPanel name, e.g. 'editorial-watchlist'. Used to save and recall the query set.
queriesNoQueries to save under this panel. Omit to read the existing panel.
domainNoDefault domain to track for this panel, e.g. 'automatelab.tech'.
actionNo'save' writes the panel, 'load' returns an existing panel, 'list' enumerates all panels.save
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that panels are persisted under a specific file path, implying stateful behavior. However, it does not explain if save overwrites, if there are any destructive side effects, or any auth/rate limit info.

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 three concise sentences with no wasted words. The first sentence states the core purpose, the second gives usage guidance, and the third adds storage context. It is front-loaded and efficient.

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 tool has 4 parameters, 3 actions, no output schema. The description covers all actions, gives example, explains persistence. It doesn't describe return values for load/list, but the actions imply it returns queries. Could mention that load returns a panel object, but it's sufficient for an AI agent.

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%, so baseline is 3. The description adds value by explaining the relationship between action and queries parameters, and gives a concrete example ('editorial-watchlist') and the storage location, which goes beyond schema descriptions.

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 saves, loads, or lists named query panels, using specific verbs and resources. It distinguishes from siblings by focusing on panel management, which is unique among the listed tools.

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?

The description explicitly explains when to use each action (save, load, list) and provides context for why you'd use panels (monitor queries over time). It does not explicitly say when not to use, but the actions are self-explanatory.

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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