Skip to main content
Glama

create_context_receipt_from_path

Create a deterministic context receipt from a local text file or directory, ingesting supported document types (.md, .txt, .rst) to optimize AI coding agent context windows.

Instructions

Create a Context Receipt from a local document file or directory.

Supports text-like documents currently handled by the local receipt ingester (.md, .txt, .rst). The result is deterministic and local.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
queryYes
chunk_tokensNo
token_budgetNo
overlap_tokensNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

The description mentions 'deterministic and local', which is a useful behavioral trait. However, without annotations, it does not disclose side effects (e.g., file system writes), required permissions, or response format. The safety profile is under-communicated.

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 short (two sentences) and front-loaded with the main purpose. It adds relevant detail about supported formats and determinism without extraneous text.

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 5 parameters with no schema descriptions, no annotations, and a context tool that presumably returns a receipt, the description is incomplete. It fails to explain the output or how parameters like 'query' and chunking settings affect behavior.

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?

With 0% schema description coverage, the description should compensate by explaining parameters. Only 'path' is implied; 'query', 'chunk_tokens', 'token_budget', and 'overlap_tokens' are not mentioned at all, leaving the agent without guidance on how to use them.

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 tool name and description clearly state it creates a Context Receipt from a local file or directory. The supported file types (.md, .txt, .rst) are listed, which distinguishes it from create_context_receipt (likely for non-local sources). However, explicit differentiation from the sibling tool is missing.

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

Usage Guidelines3/5

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

The description implies usage for local text-like documents via the 'local receipt ingester'. It does not specify when not to use the tool or provide alternatives, leaving the agent to infer context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/juyterman1000/entroly'

If you have feedback or need assistance with the MCP directory API, please join our Discord server