get_file_spec
Get the detailed specification for one AI readiness output file (e.g. llms.txt, ai-entities.json)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | Output filename, e.g. llms.txt or .well-known/ai-plugin.json |
AI Readiness Kit
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description merely says 'get' which implies read-only, but does not explicitly state side effects, permissions, or behavioral traits. Adequate but not detailed.
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?
A single sentence with no extraneous words, clearly presenting the tool's purpose and an example.
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 no output schema, the description lacks explanation of what the 'detailed specification' contains, error handling, or expected return format. Incomplete for a tool that returns structured data.
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 coverage is 100% and the description's parameter examples mirror the schema's. The description adds no new semantic information beyond what the schema provides.
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 clearly states the verb 'Get' and the resource 'detailed specification for one AI readiness output file', with examples that differentiate it from sibling tools like generate_ai_readiness_files or list_output_files.
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?
No guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description only states what it does without contextual usage advice.
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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TDQS
A3.7/5.0
Disambiguation5/5
Each tool targets a distinct purpose: generating the full workflow, converting a specific file, retrieving specs, classification guide, instructions, or listing files. No overlapping functionality.
Naming Consistency5/5
All tools use consistent snake_case with verb_noun structure (generate_*, get_*, list_*), making the set predictable and easy to navigate.
Tool Count5/5
With 6 tools, the set covers the core informational needs (specs, guide, instructions, file list) plus the main workflow generator and a format converter, which is well-scoped for the domain.
Completeness2/5
The tool set lacks tools for actually writing or outputting the generated files; 'generate_ai_readiness_files' returns a workflow but no file creation tool exists, leaving a significant gap in the lifecycle.