econ-r-mcp

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run_regression_fixest

Run a fixest regression model with fixed effects and clustered standard errors. Save coefficient tables, logs, and model metadata for reproducible econometric analysis.

Instructions

Run a fixest regression and save coefficient tables, logs, model object, and metadata.

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": {
      "params": "<params>"
    },
    "name": "run_regression_fixest"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 27, 2026

TDQS

C2.9/5.0

Behavior3/5

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

Annotations are present (readOnlyHint=false, destructiveHint=false) but the description adds that outputs are saved, which implies side effects. However, it does not clarify whether existing files are overwritten, where outputs are stored, or any other behavioral details. The added value beyond annotations is limited.

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 a single, direct sentence with no wasted words. It is front-loaded with the primary action. However, it is arguably too terse and could benefit from additional context without sacrificing conciseness.

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?

Given the existence of many sibling regression tools and the presence of an output schema, the description is insufficient. It does not explain what fixest is, when to choose it, or what the output schema contains. The agent lacks enough information to use this tool effectively in context.

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?

The schema has many parameters (like formula, fixed_effects, cluster_vars) but schema description coverage is reported as 0%. The tool description does not explain any parameter meanings or usage beyond the generic action, so it adds little meaning for parameter selection.

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?

The description clearly states the action ('Run a fixest regression') and the outputs saved (coefficient tables, logs, etc.), providing a specific verb and resource. However, it does not distinguish this tool from sibling regression tools like run_did or run_iv, which is a gap for an AI agent deciding which tool to use.

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?

The description provides no guidance on when to use this tool versus alternatives. It lacks any mention of context, prerequisites, or exclusions, leaving the agent without decision-making support.

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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