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thein-art

mcp-server-peecai

by thein-art

List Chats

list_chats
Read-onlyIdempotent

Retrieve AI chat interactions tracked by Peec AI. Filter by dates, brand, prompt, model, or channel to scope results.

Instructions

List AI chat interactions tracked by Peec AI. Returns up to limit results (default: 100). Recommended: use date filters to scope results. Returns chat IDs, prompt/model refs, and dates. Without date filters, returns all chats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoProject ID (uses PEECAI_PROJECT_ID env if omitted). Call list_projects to find IDs.
start_dateNoStart date filter (YYYY-MM-DD). Omit for no lower bound.
end_dateNoEnd date filter (YYYY-MM-DD). Omit for no upper bound.
brand_idNoFilter by brand ID. Use list_brands to find IDs.
prompt_idNoFilter by prompt ID. Use list_prompts to find IDs.
model_idNoFilter by model ID. Use list_models to find IDs.
model_channel_idNoFilter by model channel ID (e.g. openai-0, perplexity-0). Use list_model_channels to find IDs.
limitNoMax results (1-10000, default: 100)
offsetNoResults to skip

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_summaryYesHuman-readable summary of the result
chatsYes
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint true. The description adds useful context: default limit of 100, recommendation to use date filters, and that it returns all chats without filters. No contradictions.

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?

Three concise sentences, front-loaded with the main purpose, then key behavior (limit, date filters recommendation, return fields). No wasted words.

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?

For a listing tool with 9 optional parameters and an output schema, the description covers the essential behavior: what it returns, default limit, and filtering recommendation. Could mention offset/pagination, but output schema likely addresses that. Relatively complete.

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 coverage is 100% with each parameter described. The description adds no additional parameter-level meaning beyond mentioning 'date filters' generically. Baseline score of 3 is appropriate since schema carries the burden.

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?

Clearly states it lists AI chat interactions, mentions returned fields (chat IDs, prompt/model refs, dates), and distinguishes itself from siblings like get_chat_content which retrieves individual chat content.

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?

Recommends using date filters to scope results and warns that without filters it returns all chats, providing practical guidance. However, it does not explicitly state when not to use this tool or identify alternative tools for specific needs.

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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