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compute_file_hash

Compute a cryptographic hash of a local file to get the fingerprint needed for blockchain timestamp proofs or to confirm a file has not changed. The file is hashed locally and never uploaded.

Instructions

Compute the cryptographic hash of a local file.

The file is read and hashed locally; its contents are never uploaded or transmitted. SHA-256 (the default) is the algorithm used by Bitcoin, Polygon, and blockchain timestamp services such as ProofLedger. Use this to obtain the fingerprint that a timestamp proof anchors, or to confirm a file has not changed.

Args: file_path: Path to the file on the local machine. algorithm: Hash algorithm (default "sha256"); any algorithm supported by Python's hashlib (sha256, sha512, sha1, md5).

Returns: The hex-encoded hash digest.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
algorithmNosha256
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does well by stating that the file is hashed locally and never uploaded, that SHA-256 is the default, and that the result is a hex-encoded digest. It does not discuss edge cases like large-file handling, but for this tool the core behavior is well covered.

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 well-structured with purpose first, followed by Args and Returns. The blockchain context sentence is somewhat extra but still relevant to the intended use case, and the overall length is appropriate.

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

Completeness5/5

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

For a simple two-parameter tool, the description covers everything needed to call it correctly: the file path, optional algorithm, default behavior, local-only processing, and return format. The output schema exists, so the return value description is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully explain the parameters, and it does. It explains file_path as a local path and algorithm with its default and valid Python hashlib options, adding substantial meaning beyond the bare schema.

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 a specific action and resource: computing the cryptographic hash of a local file. It even provides use cases (obtaining a proof fingerprint, confirming a file has not changed), but it does not explicitly differentiate itself from siblings like verify_hash or verify_file beyond the implied compute-vs-verify distinction.

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?

The description gives concrete scenarios for when to use the tool: obtaining the fingerprint for a timestamp proof or confirming a file has not changed. It does not explicitly mention when not to use it or name alternative tools, but the context is clear enough to guide selection among siblings.

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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