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fastmcp-me

Tenzir MCP Server

by fastmcp-me

Generate a TQL parser

make_parser
Idempotent

Turn sample log events into a TQL parser for JSON, CSV, syslog, or key-value formats. Get step-by-step guidance for operator selection and type conversion to produce structured data.

Instructions

Generate a TQL parser for the given log format.

Use this tool when:

  • You have sample log events and need to parse them into structured data

  • You're starting a new parser for JSON, CSV, syslog, or key-value logs

  • You want guidance on format detection and TQL operator selection

  • You need to infer types and create proper schema transformations

This tool provides a complete workflow with step-by-step instructions for:

  1. Analyzing log format and structure

  2. Selecting appropriate TQL operators

  3. Generating parsing code with type conversions

  4. Creating a package with the parser

  5. Testing the parser with sample data

Follow the workflow instructions provided in the response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ctxNo
sampleYesSample log events to generate parser from
Behavior3/5

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

Annotations already declare readOnlyHint false and idempotentHint true. The description adds that it provides a step-by-step workflow and instructs the agent to follow the instructions in the response. This adds some context about the tool's behavior, but it does not fully disclose the nature of side effects (e.g., package creation) or clarify what happens on repeated calls. No contradiction with annotations, so a 3 is appropriate.

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 longer than typical but well-structured with a clear first sentence, bulleted use cases, and a numbered workflow list. Every segment contributes meaning, though it could be trimmed without losing value. Front-loading the purpose helps; the overall organization earns a 4.

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 there is no output schema, the description should explain what the tool returns. It lists workflow steps and says to follow instructions, but it does not explicitly state whether the response contains generated code, a package, or a plan. The description provides a decent overview but leaves ambiguity about the exact output format and any potential errors or limitations, warranting a 3.

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

Parameters2/5

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

Schema descriptions cover only the 'sample' parameter (50% coverage), and the description does not add any additional meaning for either parameter. The 'ctx' parameter is completely undocumented. The description mentions log format and sample events but does not explain the parameters or their syntax beyond what the schema already provides, failing to compensate for the missing parameter documentation.

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 starts with 'Generate a TQL parser for the given log format', which clearly states the specific action (generate) and resource (TQL parser). The use case bullets further clarify the tool's scope, and it is distinct from sibling tools like make_ocsf_mapping, which handles mappings rather than parsers.

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 includes an explicit 'Use this tool when' section with concrete scenarios such as having sample log events, starting a parser for JSON/CSV/syslog/key-value, or needing TQL operator guidance. It provides clear context but does not mention when not to use it or explicitly name alternatives, so it falls short of a 5.

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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