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hag

by heyuan29171
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect_project

Scans a project to report detected languages, candidate files with usable line counts, and skipped files with reasons, so you can verify sufficient source material before generating submission documents.

Instructions

Scan a project and report what would be submitted: detected languages, candidate files with their usable line counts, and every skipped file with the reason. Call this first to see whether the project has enough material and to understand what was excluded.

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": {
      "project_root": "<project_root>"
    },
    "name": "inspect_project"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_rootYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: "scan and report" frames this as a non-mutating inspection, and it discloses that excluded files are surfaced with their reasons, which is the behaviorally important detail. It does not state permission or path-handling expectations, leaving a small gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero filler, with the primary action stated first and the sequencing advice second. Every clause adds information an agent can act on.

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

Completeness4/5

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

An output schema exists and the description independently summarizes the return contents, so return-value explanation is not owed. For a one-parameter, low-complexity tool this is nearly complete; only the meaning and format of project_root are left unaddressed.

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 0% for the single required parameter, so the description should compensate and does not—project_root is never mentioned, nor is whether it is an absolute path, relative path, or workspace identifier. The verb "Scan a project" only loosely implies it, so this is baseline adequacy rather than added meaning.

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?

Uses a concrete verb ("Scan") plus resource ("a project") and enumerates exactly what the report contains: detected languages, candidate files with usable line counts, and skipped files with reasons. That specificity lets an agent distinguish it from siblings like estimate_material or generate_materials without opening any schema.

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?

"Call this first to see whether the project has enough material" gives explicit ordering and intent, which is strong guidance in a workflow with check_fields, estimate_material and generate_materials. It stops short of naming when to prefer a sibling instead, so it is not a full 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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