CommonTrace MCP Server

search_traces

Read-only

Find coding traces using natural language queries and tag filters to access shared programming knowledge and solutions.

Instructions

Search CommonTrace for coding traces matching a natural language query and/or tags.

Args: query: Natural language description of what you're looking for tags: Filter by tags like language, framework, or task type (AND semantics) limit: Maximum number of results (1-50, default 10) context: Searcher's environment context for relevance boosting (e.g. {"language": "python", "os": "linux"}) include_expired: Include expired traces (de-ranked) or exclude entirely (default True)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "search_traces"
  }
}

Input Schema

Name	Required	Description	Default
`query`	No
`tags`	No
`limit`	No
`context`	No
`include_expired`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Mar 25, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Adds valuable behavioral details beyond annotations: explains 'de-ranked' handling of expired traces, 'AND semantics' for tag filtering, and 'relevance boosting' via context. Does not contradict readOnlyHint/openWorldHint annotations.

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?

Well-structured with purpose front-loaded in first sentence, followed by Args section. No redundant repetition of tool name. Efficient given necessity of inline parameter documentation.

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?

Comprehensive coverage of 5 optional parameters including nested object semantics. Since output schema exists, no need to describe returns. Addresses complexity despite zero native schema descriptions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the Args section fully documents all 5 parameters with types, constraints (limit range 1-50), default values, and concrete examples (context object with language/os). Completely compensates for schema deficiency.

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?

Specific verb 'Search' targets clear resource 'CommonTrace' for 'coding traces'. Scope (natural language query and/or tags) distinguishes from sibling get_trace (retrieval by ID) and list_tags (tag enumeration).

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?

Implies usage context through 'matching a natural language query' but lacks explicit when-to-use guidance versus alternatives like get_trace or list_tags. No prerequisites or exclusion criteria stated.

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

Deploy Server