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SynAgent MCP - Cross-Agent CLI Bridge

codex_analyze

Analyze code architecture, dependencies, and structure in a read-only sandbox to answer targeted questions and surface design issues before making changes.

Instructions

Perform deep architectural, dependency, and structural code analysis in read-only sandbox mode using OpenAI Codex.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe specific question, architectural aspect, or focus area to analyze.
modelNoModel to use (default: "gpt-6.1-sol", options: "gpt-6.1-sol", "gpt-6.0-sol", "luna", "astra").
file_pathsNoOptional list of files or directories to inspect.
user_confirmedNoMandatory true confirmation if using the top-tier "astra" model.
workspace_pathNoOptional absolute path to workspace root.
reasoning_effortNoReasoning effort depth (default: "high").

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose one important trait: 'read-only sandbox mode,' which reassures the agent no mutations occur. It adds nothing about cost, latency, model-tier implications, or that astra requires confirmation, so the disclosure is only partial.

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?

A single front-loaded sentence with zero filler, stating the action and its scope immediately. It is efficient, though arguably too terse given the tool's complexity.

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

Completeness2/5

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

For a 6-parameter tool with no annotations and no output schema, the description omits the astra confirmation requirement, guidance on choosing a model, and anything about what the analysis returns. An agent could call it, but not with full understanding of the parameters' significance.

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 description coverage is 100%, so the baseline is 3. The description adds no parameter-level meaning beyond the schema, e.g., nothing about the user_confirmed/astra gating or how reasoning_effort affects analysis depth.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (analyze) and scopes the resource precisely: architectural, dependency, and structural code analysis. However, it never distinguishes itself from the close sibling codex_review_code, so an agent cannot tell them apart on description alone.

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

Usage Guidelines2/5

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

There is no when-to-use, when-not-to-use, or named alternative. The phrases 'deep' and 'architectural' hint at the intended scope but leave the routing decision against codex_review_code/codex_consult to inference.

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