Beeper Texts MCP Server

by tetmin

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Python

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search_chat_names

Search for chats by name or title, filter results by label such as inbox, archive, or favourite.

Instructions

Search for chats by name/title with filtering by label.

Args:
    query: Chat name or partial name to search for
    label: Filter results by chat type: 'inbox', 'archive', 'favourite', 'all' (default 'all')
    limit: Maximum number of results to return (default 25)

Returns:
    List of Chat objects matching the search criteria

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_chat_names"
  }
}

Input Schema

Name	Required	Default
`label`	No	all
`limit`	No
`query`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.2Jul 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains that the tool returns a list of Chat objects and describes parameters, but lacks details on search behavior (e.g., fuzzy matching, case sensitivity), error handling, or rate limits. Some behavioral information is present but not comprehensive.

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 description is structured with Args and Returns sections, making it easy to scan. It is concise without extraneous words, but could be slightly more front-loaded with the core action. Overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no annotations, and an output schema exists, the description covers the main purpose and parameters but lacks behavioral details like search semantics, edge cases, or guidance on when to use this versus list_chats or search_message_contents. It is adequate but not complete.

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 description adds meaning beyond the input schema for all three parameters, clarifying that query is a partial name match, label filters by chat type with enumerated options, and limit caps results. Since schema coverage is 0%, this provides essential context.

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 searches for chats by name/title with label filtering, using a specific verb and resource. It distinguishes from sibling tools like search_message_contents (searches messages) and list_chats (lists all chats).

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool over alternatives or provide when-not-to-use guidance. The context is implied by the tool name and sibling names, but no explicit exclusions or comparisons are given.

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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